References
Abudayyeh, O. O., Gootenberg, J. S., Essletzbichler, P., Han, S., Joung,
J., Belanto, J. J., Verdine, V., Cox, D. B. T., Kellner, M. J., Regev,
A., Lander, E. S., Voytas, D. F., Ting, A. Y., & Zhang, F. (2017).
RNA targeting with CRISPR–Cas13.
Nature, 550(7675), 280–284. https://doi.org/10.1038/nature24049
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen,
Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., Pak,
R. A., Gray, A. N., Gross, C. A., Dixit, A., Parnas, O., Regev, A.,
& Weissman, J. S. (2016). A multiplexed single-cell
CRISPR screening platform enables systematic dissection of
the unfolded protein response. Cell, 167(7),
1867–1882.e21. https://doi.org/10.1016/j.cell.2016.11.048
Alaburde, A., Ivaska, J., Kaspute, G., & Ivaskiene, T. (2025).
Impact of regulatory measures on the approval timelines of advanced
therapy medicinal products by the European Medicines
Agency. Frontiers in Medicine, 12, 1623689. https://doi.org/10.3389/fmed.2025.1623689
Alexandrov, L. B., Kim, J., Haradhvala, N. J., Huang, M. N., Tian Ng, A.
W., Wu, Y., Boot, A., Covington, K. R., Gordenin, D. A., Bergstrom, E.
N., Islam, S. M. A., Lopez-Bigas, N., Klimczak, L. J., McPherson, J. R.,
Morganella, S., Sabarinathan, R., Wheeler, D. A., Mustonen, V., PCAWG
Mutational Signatures Working Group, … Stratton, M. R. (2020). The
repertoire of mutational signatures in human cancer. Nature,
578, 94–101. https://doi.org/10.1038/s41586-020-1943-3
Alkan, F., Wenzel, A., Anthon, C., Havgaard, J. H., & Gorodkin, J.
(2018). CRISPR-Cas9 off-targeting assessment with nucleic
acid duplex energy parameters. Genome Biology, 19,
177. https://doi.org/10.1186/s13059-018-1534-x
Allen, F., Crepaldi, L., Alsinet, C., Strong, A. J., Kleshchevnikov, V.,
De Angeli, P., Páleníková, P., Khodak, A., Kiselev, V., Kosicki, M.,
Bassett, A. R., Harding, H., Gaber, Y., Muñoz-Manchado, A. B., Barber,
M., & Parts, L. (2019). Predicting the mutations generated by repair
of Cas9-induced double-strand breaks. Nature
Biotechnology, 37(1), 64–72. https://doi.org/10.1038/nbt.4317
Anderson, R. A., Mitchell, R. T., Kelsey, T. W., Spears, N., Telfer, E.
E., & Wallace, W. H. B. (2015). Cancer treatment and gonadal
function: Experimental and established strategies for fertility
preservation in children and young adults. The Lancet Diabetes &
Endocrinology, 3(7), 556–567. https://doi.org/10.1016/S2213-8587(15)00039-X
Angermueller, C., Dohan, D., Belanger, D., Deshpande, R., Murphy, K.,
& Colwell, L. (2020). Model-based reinforcement learning for
biological sequence design. Proceedings of the International
Conference on Learning Representations (ICLR).
Angermueller, C., Dohan, D., Belanger, D., Murphy, K., & Colwell, L.
J. (2020). Population-based black-box optimization for biological
sequence design. Nature Methods, 17, 1173–1180. https://doi.org/10.1038/s41592-020-0965-8
Anzalone, A. V., Gao, X. D., Podracky, C. J., Nelson, A. T., Koblan, L.
W., Raguram, A., Levy, J. M., Merber, J. M., Cho, T. C., & Liu, D.
R. (2022). Programmable deletion, replacement, integration and inversion
of large DNA sequences with twin prime editing. Nature
Biotechnology, 40(5), 731–740. https://doi.org/10.1038/s41587-021-01133-w
Anzalone, A. V., Randolph, P. B., Davis, J. R., Sousa, A. A., Koblan, L.
W., Levy, J. M., Chen, P. J., Wilson, C., Newby, G. A., Raguram, A.,
& Liu, D. R. (2019). Search-and-replace genome editing without
double-strand breaks or donor DNA. Nature,
576(7785), 149–157. https://doi.org/10.1038/s41586-019-1711-4
Arbab, M., Shen, M. W., Mok, B., Wilson, C., Matuszek, Ż., Cassa, C. A.,
& Liu, D. R. (2020). Determinants of base editing outcomes from
target library analysis and machine learning. Cell,
182(2), 463–480.e30. https://doi.org/10.1016/j.cell.2020.05.037
Arnold, F. H. (2018). Directed evolution: Bringing new chemistry to
life. Angewandte Chemie International Edition, 57(16),
4143–4148. https://doi.org/10.1002/anie.201708408
Arnstein, S. R. (1969). A ladder of citizen participation. Journal
of the American Institute of Planners, 35(4), 216–224. https://doi.org/10.1080/01944366908977225
Asch, A. (2003). Disability equality and prenatal testing: Contradictory
or compatible? In Florida state university law review (Vol. 30,
pp. 315–342).
ASCO Post Staff. (2026). New European project cluster
EARLYSCAN launched to advance early detection of heritable
cancers. https://ascopost.com/news/february-2026/new-european-project-cluster-earlyscan-launched-to-advance-early-detection-of-heritable-cancers/
Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random
forests. The Annals of Statistics, 47(2), 1148–1178.
https://doi.org/10.1214/18-AOS1709
Bae, S., Park, J., & Kim, J.-S. (2014). Cas-OFFinder: A
fast and versatile algorithm that searches for potential off-target
sites of Cas9 RNA-guided endonucleases.
Bioinformatics, 30(10), 1473–1475. https://doi.org/10.1093/bioinformatics/btu048
Baltussen, R., Jansen, M. P. M., Bijl, N. de, & Stolk, E. A. (2019).
Value assessment frameworks for HTA agencies: The
organization of evidence-informed deliberative processes. Value in
Health, 22(2), 213–220. https://doi.org/10.1016/j.jval.2018.11.003
Barker, A. D., Sigman, C. C., Kelloff, G. J., Hylton, N. M., Berry, D.
A., & Esserman, L. J. (2009). I-SPY 2: An adaptive
breast cancer trial design in the setting of neoadjuvant chemotherapy.
Clinical Pharmacology & Therapeutics, 86(1),
97–100. https://doi.org/10.1038/clpt.2009.68
Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P.,
Moineau, S., Romero, D. A., & Horvath, P. (2007).
CRISPR provides acquired resistance against viruses in
prokaryotes. Science, 315(5819), 1709–1712. https://doi.org/10.1126/science.1138140
Basch, E., Deal, A. M., Dueck, A. C., Scher, H. I., Kris, M. G., Hudis,
C., & Schrag, D. (2017). Overall survival results of a trial
assessing patient-reported outcomes for symptom monitoring during
routine cancer treatment. JAMA, 318(2), 197–198. https://doi.org/10.1001/jama.2017.7156
Bate, A., & Evans, S. J. W. (2009). Quantitative signal detection
using spontaneous ADR reporting. Pharmacoepidemiology
and Drug Safety, 18(6), 427–436. https://doi.org/10.1002/pds.1742
Baylis, F., Darnovsky, M., Hasson, K., & Krahn, T. M. (2020). Human
germline and heritable genome editing: The global policy landscape.
The CRISPR Journal, 3(5), 365–377. https://doi.org/10.1089/crispr.2020.0082
Baylis, F., & Ikemoto, L. (2017). The Council
of Europe and the prohibition on human germline genome editing.
EMBO Reports, 18(12), 2084–2085. https://doi.org/10.15252/embr.201745343
Beam Therapeutics. (2025). Beam Therapeutics reports
clinical data demonstrating first-ever CRISPR correction of
a disease-causing mutation in patients with alpha-1 antitrypsin
deficiency. Corporate data presentation.
Beauchamp, T. L., & Childress, J. F. (2019). Principles of
biomedical ethics (8th ed.). Oxford University Press.
Beckert, J. (2016). Imagined futures: Fictional expectations and
capitalist dynamics. Harvard University Press.
Bellinger, A. M. et al. (2023). Safety and pharmacodynamic effects of
VERVE-101, an investigational DNA base editing
medicine designed to durably inactivate the PCSK9 gene and
lower LDL cholesterol – interim results of the Phase
1b Heart-1 trial. Circulation. https://www.vervetx.com/sites/default/files/2023-11/Verve_AHA_2023_LBS_for%20website.pdf
Benatar, S. R. (2003). Bioethics: Power and injustice: IAB
presidential address. Bioethics, 17(5–6), 387–398. https://doi.org/10.1111/1467-8519.00355
Benjamin, R. (2019). Race after technology: Abolitionist tools for
the New Jim Code. Polity Press.
Bernsen, M. R., Zanden, S. Y. van der, Heuvel, J. J. M. W. van den,
& Masereeuw, R. (2020). Pharmacogenomics as a tool to limit acute
and long-term adverse effects of chemotherapeutics. Frontiers in
Pharmacology, 11, 1184. https://doi.org/10.3389/fphar.2020.01184
Borup, M., Brown, N., Konrad, K., & Van Lente, H. (2006). The
sociology of expectations in science and technology. Technology
Analysis & Strategic Management, 18(3–4), 285–298. https://doi.org/10.1080/09537320600777002
Bostrom, N. (2003). The transhumanist FAQ: A general
introduction. Journal of Evolution and Technology,
13(1). https://www.jetpress.org/volume13/bostrom-tsfaq.html
Bowker, G. C., & Star, S. L. (1999). Sorting things out:
Classification and its consequences. MIT Press.
Brown, N. (2003). Hope against hype: Accountability in biopasts,
presents and futures. Science Studies, 16(2), 3–21.
Bryant, D. H., Bashir, A., Sinai, S., Jain, N. K., Ogden, P. J., Riley,
P. F., Church, G. M., Colwell, L. J., & Kelsic, E. D. (2021). Deep
diversification of an AAV capsid protein by machine
learning. Nature Biotechnology, 39, 691–696. https://doi.org/10.1038/s41587-020-00793-4
Buchanan, A., Brock, D. W., Daniels, N., & Wikler, D. (2000).
From chance to choice: Genetics and justice. Cambridge
University Press. https://doi.org/10.1017/CBO9780511806940
Byrne, F. et al. (2024). Prediction and prevention of late effects
in AYA cancer survivors: The PredictAYA project. European
Commission Horizon Europe Project. https://doi.org/10.3030/101214879
Calabrese, C., Amin, A., & Engel, K. (2020). Framing
CRISPR: How media shape public understanding of gene
editing. Public Understanding of Science, 29(5),
492–510. https://doi.org/10.1177/0963662520916775
Callon, M., & Rabeharisoa, V. (2003). Research “in the
wild” and the shaping of new social identities. Technology in
Society, 25(2), 193–204. https://doi.org/10.1016/S0160-791X(03)00021-6
Campa, C. C., Weisbach, N. R., Santinha, A. J., Incarnato, D., &
Platt, R. J. (2019). Multiplexed genome engineering by
Cas12a and CRISPR arrays encoded on single
transcripts. Nature Methods, 16(9), 887–893. https://doi.org/10.1038/s41592-019-0508-6
Cavazzana, M., & Mavilio, F. (2020). The role of conditioning in
hematopoietic stem-cell gene therapy. Gene Therapy,
27, 1–10. https://doi.org/10.1038/s41434-019-0103-1
CDBIO. (2022). Genome editing technologies: Final conclusions of the
re-examination of article 13 of the oviedo convention. Council of
Europe. https://www.coe.int/en/web/human-rights-and-biomedicine/-/genome-editing-technologies-final-conclusions-of-the-re-examination-of-article-13-of-the-oviedo-convention
Chandak, P., Huang, K., & Zitnik, M. (2023). Building a knowledge
graph to enable precision medicine. Scientific Data,
10, 67. https://doi.org/10.1038/s41597-023-01960-3
Chari, R., Mali, P., Moosburner, M., & Church, G. M. (2015).
Unraveling CRISPR-Cas9 genome engineering
parameters via a library-on-library approach. Nature Methods,
12(9), 823–826. https://doi.org/10.1038/nmeth.3473
Charlesworth, C. T., Deshpande, P. S., Dever, D. P., Camarena, J.,
Lemgart, V. T., Cromer, M. K., Vakulskas, C. A., Collingwood, M. A.,
Zhang, L., Buj, N. L., Gomez-Ospina, N., Mantri, S., Pavel-Dinu, M.,
Henle, J., Søndergaard, J. N., & Porteus, M. H. (2019).
Identification of preexisting adaptive immunity to Cas9
proteins in humans. Nature Medicine, 25(2), 249–254.
https://doi.org/10.1038/s41591-018-0326-x
Charon, R. (2006). Narrative medicine: Honouring the stories of
illness. Oxford University Press.
Chen, J. S., Ma, E., Harrington, L. B., Da Costa, M., Tian, X.,
Palefsky, J. M., & Doudna, J. A. (2018).
CRISPR-Cas12a target binding unleashes
indiscriminate single-stranded DNase activity.
Science, 360(6387), 436–439. https://doi.org/10.1126/science.aar6245
Chen, P. J., Hussmann, J. A., Yan, J., Knipping, F., Ravisankar, P.,
Chen, P.-F., Chen, C., Nelson, J. W., Newby, G. A., Sahin, M., Osborn,
M. J., Weissman, J. S., Adamson, B., & Liu, D. R. (2021). Enhanced
prime editing systems by manipulating cellular determinants of editing
outcomes. Cell, 184(22), 5635–5652. https://doi.org/10.1016/j.cell.2021.09.018
Chen, W., McKenna, A., Schreiber, J., Haeussler, M., Yin, Y., Agarwal,
V., Noble, W. S., & Shendure, J. (2019). Massively parallel
profiling and predictive modeling of the outcomes of
CRISPR/Cas9-mediated double-strand break
repair. Nucleic Acids Research, 47(15), 7989–8003. https://doi.org/10.1093/nar/gkz487
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum,
T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., Schneider, R.
G., Senior, A. W., Jumper, J., Hassabis, D., Kohli, P., & Avsec, Ž.
(2023). Accurate proteome-wide missense variant effect prediction with
AlphaMissense. Science, 381(6664),
eadg7492. https://doi.org/10.1126/science.adg7492
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C.,
Newey, W., & Robins, J. (2018). Double/debiased machine learning for
treatment and structural parameters. The Econometrics Journal,
21(1), C1–C68. https://doi.org/10.1111/ectj.12097
Chiesa, R., Georgiadis, C., Syed, F., et al. (2023). Base-edited
CAR7 T cells for relapsed T-cell
acute lymphoblastic leukemia. New England Journal of Medicine,
389(10), 899–910. https://doi.org/10.1056/NEJMoa2300709
Cho, S. W., Kim, S., Kim, J. M., & Kim, J.-S. (2013). Targeted
genome engineering in human cells with the Cas9
RNA-guided endonuclease. Nature Biotechnology,
31(3), 230–232. https://doi.org/10.1038/nbt.2507
Choi, S. W., Reise, S. P., Pilkonis, P. A., Hays, R. D., & Cella, D.
(2010). Efficiency of static and computer adaptive short forms compared
to full-length measures of depressive symptoms. Quality of Life
Research, 19(1), 125–136. https://doi.org/10.1007/s11136-009-9560-5
Chu, H. Y., Fong, J. H. C., Thean, D. G. L., Zhou, P., Fung, F. K. C.,
Huang, Y., & Wong, A. S. L. (2024). Accurate top protein variant
discovery via low-N pick-and-validate machine learning.
Cell Systems, 15(2), 193–203.e6. https://doi.org/10.1016/j.cels.2024.01.002
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu,
C., Chen, K., Duan, B., Gu, F., Qu, S., Huang, D., Wei, J., & Liu,
Q. (2018). DeepCRISPR: Optimized CRISPR guide
RNA design by deep learning. Genome Biology,
19, 80. https://doi.org/10.1186/s13059-018-1459-4
Collingridge, D. (1980b). The social control of technology.
Frances Pinter.
Collins, H. M., & Evans, R. (2002). The third wave of science
studies: Studies of expertise and experience. Social Studies of
Science, 32(2), 235–296. https://doi.org/10.1177/0306312702032002003
Collins, H. M., & Yearley, S. (1992). Epistemological chicken. In A.
Pickering (Ed.), Science as practice and culture (pp. 301–326).
University of Chicago Press.
Comandè, G. (2017). Regulating algorithms’ regulation? First
ethico-legal principles, problems, and opportunities of algorithms. In
T. Cerquitelli, D. Quercia, & F. Pasquale (Eds.), Transparent
data mining for big and small data (Vol. 9853, pp. 169–206).
Springer International Publishing. https://doi.org/10.1007/978-3-319-54024-5_8
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P.
D., Wu, X., Jiang, W., Marraffini, L. A., & Zhang, F. (2013).
Multiplex genome engineering using CRISPR/Cas
systems. Science, 339(6121), 819–823. https://doi.org/10.1126/science.1231143
Consortium, P., Dorp, W. van, Mulder, R. L., Heuvel-Eibrink, M. M. van
den, et al. (2024). Genetic determinants of gonadal impairment after
childhood cancer treatment. Nature Medicine, 30,
1123–1134. https://doi.org/10.1038/s41591-024-02987-3
CORDIS. (2025). Validated non-invasive liquid biopsy tests for
cancer PREDIction in LYNCH syndrome
(PREDI-LYNCH). https://doi.org/10.3030/101213916
Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Lonber, F.,
Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, H. A., Wajdan, A., et
al. (2020). The “digital twin” to enable the vision of
precision cardiology. European Heart Journal, 41(48),
4556–4564. https://doi.org/10.1093/eurheartj/ehaa159
Corrigan-Curay, J., Sacks, L., & Woodcock, J. (2018). Real-world
evidence and real-world data for evaluating drug safety and
effectiveness. JAMA, 320(9), 867–868. https://doi.org/10.1001/jama.2018.10136
Council of Europe. (1997). Convention for the protection of human
rights and dignity of the human being with regard to the application of
biology and medicine: Convention on human rights and biomedicine.
Court of Justice of the European Union. (2018). Judgment of 25
July 2018, Confédération paysanne and others,
Case C-528/16,
ECLI:EU:C:2018:583. https://infocuria.curia.europa.eu/tabs/document?source=document&docid=204387&doclang=EN
Cox, D. B. T., Gootenberg, J. S., Abudayyeh, O. O., Franklin, B.,
Kellner, M. J., Joung, J., & Zhang, F. (2017). RNA
editing with CRISPR–Cas13. Science,
358(6366), 1019–1027. https://doi.org/10.1126/science.aaq0180
Criminal law of the People’s Republic of
China, article 336. (2021).
CRISPR Therapeutics. (2024). CRISPR therapeutics provides business
update and reports fourth quarter and full year 2024 financial
results. https://crisprtx.com/about-us/press-releases-and-presentations/crispr-therapeutics-provides-business-update-and-reports-fourth-quarter-and-full-year-2024-financial-results.
Cristiano, S., Leal, A., Phallen, J., Fiksel, J., Adleff, V., Bruhm, D.
C., Jensen, S. Ø., Medina, J. E., Hruban, C., White, J. R., Palsgrove,
D. N., Niknafs, N., Anagnostou, V., Forde, P., Velculescu, V. E., et al.
(2019). Genome-wide cell-free DNA fragmentation in patients
with cancer. Nature, 570, 385–389. https://doi.org/10.1038/s41586-019-1272-6
Crowley, E., Di Nicolantonio, F., Loupakis, F., & Bardelli, A.
(2013). Liquid biopsy: Monitoring cancer-genetics in the blood.
Nature Reviews Clinical Oncology, 10(8), 472–484. https://doi.org/10.1038/nrclinonc.2013.110
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N.,
Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., Almeida, B. P.
de, Sirelkhatim, H., Richard, G., Skwark, M., Beguir, K., Lopez, M.,
& Pierrot, T. (2025). Nucleotide transformer: Building and
evaluating robust foundation models for human genomics. Nature
Methods, 22(2), 287–297. https://doi.org/10.1038/s41592-024-02523-z
Daniels, N. (2000). Normal functioning and the treatment-enhancement
distinction. Cambridge Quarterly of Healthcare Ethics,
9(3), 309–322. https://doi.org/10.1017/S0963180100903037
Darnovsky, M. (2019). The case against designer babies: The perils of
heritable genome editing. Nature Biotechnology, 37(9),
983.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler,
P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock,
C. (2017). Pooled CRISPR screening with single-cell
transcriptome readout. Nature Methods, 14(3), 297–301.
https://doi.org/10.1038/nmeth.4177
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018). Reading
Mendelian randomisation studies: A guide, glossary, and
checklist for clinicians. BMJ, 362, k601. https://doi.org/10.1136/bmj.k601
Deltcheva, E., Chylinski, K., Sharma, C. M., Gonzales, K., Chao, Y.,
Pirzada, Z. A., Eckert, M. R., Vogel, J., & Charpentier, E. (2011).
CRISPR RNA maturation by trans-encoded small
RNA and host factor RNase III.
Nature, 471(7340), 602–607. https://doi.org/10.1038/nature09886
Deverman, B. E., Pravdo, P. L., Simpson, B. P., Kumar, S. R., Chan, K.
Y., Banerjee, A., Wu, W.-L., Yang, B., Huber, N., Pasca, S. P., Bhatt,
H. R., et al. (2016). Cre-dependent selection yields
AAV variants for widespread gene transfer to the adult
brain. Nature Biotechnology, 34(2), 204–209. https://doi.org/10.1038/nbt.3440
Dewar, E. O., Ahn, C., Eraj, S., Mahal, B. A., & Sanford, N. N.
(2021). Psychological distress and cognition among long-term survivors
of adolescent and young adult cancer in the USA. Journal of Cancer
Survivorship, 15, 776–784. https://doi.org/10.1007/s11764-020-00986-0
Dinh, C. T., Tran, N. H., & Nguyen, T. D. (2020). Personalized
federated learning with moreau envelopes. NeurIPS ’20,
21394–21405. https://dl.acm.org/doi/abs/10.5555/3495724.3497520
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerber, J.,
Raychowdhury, R., Weissman, J. S., Regev, A., & Hacohen, N. (2016).
Perturb-seq: Dissecting molecular circuits with scalable single-cell
RNA profiling of pooled genetic screens. Cell,
167(7), 1853–1866.e17. https://doi.org/10.1016/j.cell.2016.11.038
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L.,
Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., Adamson, B.,
Norman, T. M., Lander, E. S., Weissman, J. S., Friedman, N., &
Regev, A. (2016). Perturb-seq: Dissecting molecular circuits with
scalable single-cell RNA profiling of pooled genetic
screens. Cell, 167(7), 1853–1866.e17. https://doi.org/10.1016/j.cell.2016.11.038
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W.,
Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin,
H. W., Listgarten, J., & Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize
off-target effects of CRISPR-Cas9. Nature
Biotechnology, 34(2), 184–191. https://doi.org/10.1038/nbt.3437
Doench, J. G., Hartenian, E., Graham, D. B., Tothova, Z., Hegde, M.,
Smith, I., Sullender, M., Ebert, B. L., Xavier, R. J., & Root, D. E.
(2014). Rational design of highly active sgRNAs for
CRISPR-Cas9-mediated gene inactivation.
Nature Biotechnology, 32(12), 1262–1267. https://doi.org/10.1038/nbt.3026
Dominguez-Valentin, M., Sampson, J. R., Seppälä, T. T., & Ten
Broeke, P., Sanne W and";";"; "; Møller. (2020). Cancer risks by gene,
age, and gender in 6350 carriers of pathogenic mismatch repair variants:
Findings from the Prospective Lynch Syndrome Database.
Genetics in Medicine, 22(1), 15–25. https://doi.org/10.1038/s41436-019-0596-9
Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., &
Torrance, G. W. (2015). Methods for the economic evaluation of
health care programmes (4th ed.). Oxford University Press.
Duan, D. (2018). Systemic AAV micro-dystrophin gene therapy for duchenne
muscular dystrophy. Molecular Therapy, 26(10),
2337–2356. https://doi.org/10.1016/j.ymthe.2018.07.011
EARLYSCAN Cluster. (2026). New European project cluster
EARLYSCAN launched to advance early detection of heritable
cancers. ASCO Post, February 2026; press release via PREDI-LYNCH
website. https://ascopost.com/news/february-2026/new-european-project-cluster-earlyscan-launched-to-advance-early-detection-of-heritable-cancers/
Editas Medicine. (2023). Editas medicine announces strategic
pipeline prioritization. Press release. https://ir.editasmedicine.com/news-releases/news-release-details/editas-medicine-announces-strategic-updates-and-portfolio
Edwards, P. N. (2010). A vast machine: Computer models, climate
data, and the politics of global warming. MIT Press.
Epstein, S. (1996). Impure science: AIDS, activism, and
the politics of knowledge. University of California Press.
European Commission. (2017). EudraLex volume 4, part
IV: Guidelines on good manufacturing practice specific to
advanced therapy medicinal products.
European Commission. (2021). Horizon Europe cluster 1
health: Annex I — key strategic orientations.
European Commission. (2024). EU Mission: cancer.
https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe/eu-missions-horizon-europe/eu-mission-cancer_en
European Commission. (2025a). European Health Data
Space implementation timeline. https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en
European Commission. (2025b). Validated non-invasive liquid biopsy
tests for cancer PREDIction in LYNCH syndrome
– CORDIS. https://cordis.europa.eu/project/id/101213916.
European Medicines Agency. (2023). Reflection paper on the use of
real-world data in non-interventional studies to generate real-world
evidence. https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-real-world-data-non-interventional-studies-generate-real-world-evidence_en.pdf.
European Medicines Agency. (2025a). CAT quarterly
highlights and approved ATMPs, December
2025. https://www.ema.europa.eu/en/documents/committee-report/cat-quarterly-highlights-approved-atmps-december-2025_en.pdf
European Medicines Agency. (2025b). Guideline on quality,
non-clinical and clinical requirements for investigational advanced
therapy medicinal products in clinical trials.
European Parliament and Council. (2001). Directive 2001/18/EC on the
deliberate release into the environment of genetically modified
organisms: L 106 (pp. 1–39). Official Journal of the European
Communities. https://eur-lex.europa.eu/eli/dir/2001/18/oj/eng
European Parliament and Council. (2014). Regulation (EU) no 536/2014
on clinical trials on medicinal products for human use: L 158 (pp.
1–76). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32014R0536
European Parliament and Council. (2024). Regulation (EU) 2024/1689
laying down harmonised rules on artificial intelligence (AI act):
L. Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
European Parliament and Council. (2025). Regulation (EU) 2025/327 on
the european health data space. Official Journal of the European
Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32025R0327
European Parliament and Council of the European Union. (2007).
Regulation (EC) no 1394/2007 on advanced therapy medicinal products:
L 324 (pp. 121–137). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32007R1394
European Parliament and Council of the European Union. (2016).
Regulation (EU) 2016/679 (general data protection regulation).
L 119, 1–88. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679
European Parliament and Council of the European Union. (2017a).
Regulation (EU) 2017/745 on medical devices: L 117 (pp. 1–175).
Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32017R0745
European Parliament and Council of the European Union. (2017b).
Regulation (EU) 2017/746 on in vitro diagnostic medical devices: L
117 (pp. 176–332). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32017R0746
European Parliament and Council of the European Union. (2024).
Regulation (EU) 2024/1689 laying down harmonised rules
on artificial intelligence (AI Act).
Official Journal of the European Union, L series.
European Parliament and Council of the European Union. (2025).
Regulation (EU) 2025/327 establishing the
European Health Data Space. Official Journal of the
European Union.
Faden, R. R., & Beauchamp, T. L. (1986). A history and theory of
informed consent. Oxford University Press.
FDA, Health Canada, & MHRA. (2021). Good machine learning
practice for medical device development: Guiding principles.
Finan, C., Gaulton, A., Kruger, F. A., Lumbers, R. T., Shah, T.,
Engmann, J., Galver, L., Kelley, R., Karlsson, A., Santos, R.,
Overington, J. P., Hingorani, A. D., & Casas, J. P. (2017). The
druggable genome and support for target identification and validation in
drug development. Science Translational Medicine,
9(383), eaag1166. https://doi.org/10.1126/scitranslmed.aag1166
Findlay, G. M., Daza, R. M., Martin, B., Zhang, M. D., Leith, A. P.,
Gasperini, M., Janizek, J. D., Huang, X., Starita, L. M., &
Shendure, J. (2018). Accurate classification of BRCA1
variants with saturation genome editing. Nature,
562(7726), 217–222. https://doi.org/10.1038/s41586-018-0461-z
Finn, J. D., Smith, A. R., Patel, M. C., Shaw, L., Youniss, M. R.,
Heteren, J. van, Dirstine, T., Ciullo, C., Lescarbeau, R., Seitzer, J.,
Shah, R. R., Shah, A., Ling, D., Growe, J., Pink, M., Rohde, E., Wood,
K. M., Salomon, W. E., Harrington, W. F., … Morrissey, D. V. (2018). A
single administration of CRISPR/Cas9 lipid
nanoparticles achieves robust and persistent in vivo genome editing.
Cell Reports, 22(9), 2227–2235. https://doi.org/10.1016/j.celrep.2018.02.014
Forget, B. G. (1998). Molecular basis of hereditary persistence of fetal
hemoglobin. Annals of the New York Academy of Sciences,
850(1), 38–44. https://doi.org/10.1111/j.1749-6632.1998.tb10460.x
Frangoul, H. et al. (2021). CRISPR-Cas9 gene editing for sickle cell
disease and β-thalassemia.
New England Journal of Medicine, 384(3), 252–260. https://doi.org/10.1056/NEJMoa2031054
Frangoul, H., Altshuler, D., Cappellini, M. D., Chen, Y.-S., Domm, J.,
Eustace, B. K., Foell, J., Fuente, J. de la, Gruber, S., Handgretinger,
R., et al. (2021). CRISPR–Cas9 gene editing
for sickle cell disease and β-thalassemia. New England
Journal of Medicine, 384(3), 252–260. https://doi.org/10.1056/NEJMoa2031054
Frangoul, H., Locatelli, F., Sharma, A., et al. (2024). Exagamglogene
autotemcel for severe sickle cell disease. New England Journal of
Medicine, 390(18), 1649–1662. https://doi.org/10.1056/NEJMoa2309676
Fricker, M. (2007). Epistemic injustice: Power and the ethics of
knowing. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198237907.001.0001
Fukuyama, F. (2002). Our posthuman future: Consequences of the
biotechnology revolution. Farrar, Straus; Giroux.
Garrison, L. P., Jiao, B., & Engel-Nitz, N. M. (2021). Gene therapy
may not be as expensive as people think: Challenges in assessing the
value of single and short-term therapies. Journal of Managed Care
& Specialty Pharmacy, 27(5), 674–681. https://doi.org/10.18553/jmcp.2021.27.5.674
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim,
S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S.,
Trapnell, C., Ahfeldt, T., & Shendure, J. (2019). A genome-wide
framework for mapping gene regulation via cellular genetic screens.
Cell, 176(1–2), 377–390.e19. https://doi.org/10.1016/j.cell.2018.11.029
Gaudelli, N. M., Komor, A. C., Rees, H. A., Packer, M. S., Badran, A.
H., Bryson, D. I., & Liu, D. R. (2017). Programmable base editing of
A·T to G·C in
genomic DNA without DNA cleavage.
Nature, 551(7681), 464–471. https://doi.org/10.1038/nature24644
Georgiadis, A., Durham, J. N., Keefer, L. A., Bartlett, B. R., Zielonka,
M., Murphy, D., White, J. R., Lu, S., Verner, E. L., Rber, F., Paschen,
A., Diaz, L. A., Silliman, N., & Velculescu, V. E. (2019).
Noninvasive detection of microsatellite instability and high tumor
mutation burden in cancer patients treated with PD-1
blockade. Clinical Cancer Research, 25(23), 7024–7034.
https://doi.org/10.1158/1078-0432.CCR-19-1372
Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres,
S. E., Stern-Ginossar, N., Brandman, O., Whitehead, E. H., Doudna, J.
A., Lim, W. A., Weissman, J. S., & Qi, L. S. (2013).
CRISPR-mediated modular RNA-guided regulation
of transcription in eukaryotes. Cell, 154(2), 442–451.
https://doi.org/10.1016/j.cell.2013.06.044
Gillmore, J. D., Gane, E., Taubel, J., Kao, J., Fontana, M., Maitland,
M. L., et al. (2021). CRISPR-Cas9 in vivo gene
editing for transthyretin amyloidosis. New England Journal of
Medicine, 385(6), 493–502. https://doi.org/10.1056/NEJMoa2107454
Gillmore, J. D., Gane, E., Taubel, J., Kao, J., Fontana, M., Maitland,
M. L., Seitzer, J., O’Connell, D., Walsh, K. R., Wood, K., Phillips, J.,
Xu, Y., Amaral, A., Boyd, A. P., Cehelsky, J. E., McKee, M. D.,
Schiermeier, A., Hutchaleelaha, A., Beattie, B. J., … Sardh, E. (2021).
CRISPR-Cas9 in vivo gene editing for
transthyretin amyloidosis. New England Journal of Medicine,
385(6), 493–502. https://doi.org/10.1056/NEJMoa2107454
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A
systematic review of frequency, effect mediators, and mitigators.
Journal of the American Medical Informatics Association,
19(1), 121–127. https://doi.org/10.1136/amiajnl-2011-000089
Goldsack, J. C., Ernst, S., Dockendorf, M. F., Perumal, T. M., &
Vandendriessche, B. (2023). Digital endpoints in clinical trials:
Building the business case for systematic adoption. Nature,
620, 109–118. https://doi.org/10.1038/d41573-026-00033-5
Gonatopoulos-Pournatzis, T., Aregger, M., Brown, K. R., Farhangmehr, S.,
Braunschweig, U., Ward, H. N., Ha, K. C. H., Weiss, A., Billmann, M.,
Durbic, T., Myers, C. L., Blencowe, B. J., & Moffat, J. (2020).
Genetic interaction mapping and exon-resolution functional genomics with
a hybrid Cas9–Cas12a platform. Nature
Biotechnology, 38, 638–648. https://doi.org/10.1038/s41587-020-0437-z
González, J., Dai, Z., Hennig, P., & Lawrence, N. (2016). Batch
Bayesian optimization via local penalization.
Proceedings of the 19th International Conference on Artificial
Intelligence and Statistics (AISTATS), 648–657. https://proceedings.mlr.press/v51/gonzalez16a.pdf
Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy,
A. J., Joung, J., Verdine, V., Donghia, N., Daringer, N. M., Freije, C.
A., Myhrvold, C., Bhatt, R. P., Livny, J., Regev, A., Koonin, E. V.,
Hung, D. T., Sabeti, P. C., Collins, J. J., & Zhang, F. (2017).
Nucleic acid detection with
CRISPR-Cas13a/C2c2.
Science, 356(6336), 438–442. https://doi.org/10.1126/science.aam9321
Government of Japan. (2013). Act on the safety of regenerative
medicine. Act No. 85 of November 27, 2013. https://www.japaneselawtranslation.go.jp/en/laws/view/2837
Green, D. M., Nolan, V. G., Goodman, P. J., Whitton, J. A., Srivastava,
D., Leisenring, W. M., Neglia, J. P., Sklar, C. A., Kaste, S. C.,
Hudson, M. M., Diller, L. R., Stovall, M., Donaldson, S. S., &
Robison, L. L. (2014). The cyclophosphamide equivalent dose as an
approach for quantifying alkylating agent exposure. Pediatric Blood
& Cancer, 61(1), 53–67. https://doi.org/10.1002/pbc.24679
Guston, D. H. (2014). Understanding “anticipatory
governance.” Social Studies of Science, 44(2),
218–242. https://doi.org/10.1177/0306312713508669
Habermas, J. (2003). The future of human nature. Polity Press.
Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J.,
Renaud, J.-B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent,
J., Joly, J.-S., & Concordet, J.-P. (2016). Evaluation of off-target
and on-target scoring algorithms and integration into the guide
RNA selection tool CRISPOR. Genome
Biology, 17, 148. https://doi.org/10.1186/s13059-016-1012-2
Haraway, D. (1988). Situated knowledges: The science question in
feminism and the privilege of partial perspective. Feminist
Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
Harden, K. P. (2021). The genetic lottery: Why DNA
matters for social equality. Princeton University Press.
Harpaz, R., DuMouchel, W., Shah, N. H., Madigan, D., Ryan, P., &
Friedman, C. (2012). Novel data-mining methodologies for adverse drug
event discovery and analysis. Clinical Pharmacology &
Therapeutics, 91(6), 1010–1021. https://doi.org/10.1038/clpt.2012.50
Hart, T., & Moffat, J. (2016). BAGEL: A computational
framework for identifying essential genes from pooled library screens.
BMC Bioinformatics, 17, 164. https://doi.org/10.1186/s12859-016-1015-8
Heijden, K. van der. (2005). Scenarios: The art of strategic
conversation (2nd ed.). John Wiley & Sons.
Heil, B. J., Hoffman, M. M., Markowetz, F., Lee, S.-I., Greene, C. S.,
& Hicks, S. C. (2021). Reproducibility standards for machine
learning in the life sciences. Nature Methods, 18(10),
1132–1135. https://doi.org/10.1038/s41592-021-01256-7
Heitzer, E., Haque, I. S., Roberts, C. E. S., & Speicher, M. R.
(2019). Current and future perspectives of liquid biopsies in
genomics-driven oncology. Nature Reviews Genetics,
20(2), 71–88. https://doi.org/10.1038/s41576-018-0071-5
Hernán, M. A., & Robins, J. M. (2020). Causal inference: What
if. Chapman & Hall/CRC.
Hilton, I. B., D’Ippolito, A. M., Vockley, C. M., Thakore, P. I.,
Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2015). Epigenome
editing by a CRISPR-Cas9-based
acetyltransferase activates genes from promoters and enhancers.
Nature Biotechnology, 33(5), 510–517. https://doi.org/10.1038/nbt.3199
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L.,
Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017).
Systematic integration of biomedical knowledge prioritizes drugs for
repurposing. eLife, 6, e26726. https://doi.org/10.7554/eLife.26726
Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M., &
Davey Smith, G. (2021). Integrating genomics with biomarkers and
therapeutic targets to invigorate cardiovascular drug development.
Nature Reviews Cardiology, 18(6), 435–453. https://doi.org/10.1038/s41569-020-00493-1
Horlbeck, M. A., Gilbert, L. A., Villalta, J. E., Adamson, B., Pak, R.
A., Chen, Y., Fields, A. P., Park, C. Y., Corn, J. E., Kampmann, M.,
& Weissman, J. S. (2016). Compact and highly active next-generation
libraries for CRISPR-mediated gene repression and
activation. eLife, 5, e19760. https://doi.org/10.7554/eLife.19760
Hsu, P. D., Scott, D. A., Weinstein, J. A., Ran, F. A., Konermann, S.,
Agarwala, V., Li, Y., Fine, E. J., Wu, X., Shalem, O., Cradick, T. J.,
Marraffini, L. A., Bao, G., & Zhang, F. (2013). DNA
targeting specificity of RNA-guided Cas9
nucleases. Nature Biotechnology, 31(9), 827–832. https://doi.org/10.1038/nbt.2647
Huang, T. P., Zhao, K. T., Miller, S. M., Gaudelli, N. M., Oakes, B. L.,
Fellmann, C., Savage, D. F., & Liu, D. R. (2019). Circularly
permuted and PAM-modified Cas9 variants
broaden the targeting scope of base editors. Nature
Biotechnology, 37(6), 626–631. https://doi.org/10.1038/s41587-019-0134-y
Hurlbut, J. B. (2017). Experiments in democracy: Human embryo
research and the politics of bioethics. Columbia University Press.
Husson, O., Thomas, D. M., & Graaf, W. T. A. van der. (2023).
Adolescent and young adult cancer survivorship and aging: The next step
to take? Cancer Survivorship Research & Care,
1(1), 2234818. https://doi.org/10.1080/28352610.2023.2234818
Ishino, Y., Shinagawa, H., Makino, K., Amemura, M., & Nakata, A.
(1987). Nucleotide sequence of the iap gene, responsible for alkaline
phosphatase isozyme conversion in Escherichia
coli, and identification of the gene product. Journal of
Bacteriology, 169(12), 5429–5433. https://doi.org/10.1128/jb.169.12.5429-5433.1987
Jansen, R., Embden, J. D. A. van, Gaastra, W., & Schouls, L. M.
(2002). Identification of genes that are associated with
DNA repeats in prokaryotes. Molecular
Microbiology, 43(6), 1565–1575. https://doi.org/10.1046/j.1365-2958.2002.02839.x
Jasanoff, S. (2004a). States of knowledge: The co-production of
science and social order. Routledge.
Jasanoff, S. (2004b). States of knowledge: The co-production of
science and social order. Routledge.
Jasanoff, S. (2004c). States of knowledge: The co-production of
science and the social order. Routledge. https://doi.org/10.4324/9780203413845
Jasanoff, S. (2005b). Designs on nature: Science and democracy in
Europe and the United States. Princeton
University Press.
Jasanoff, S. (2005a). Designs on nature: Science and democracy in
Europe and the United States. Princeton
University Press.
Jasanoff, S., & Hurlbut, J. B. (2018b). A global observatory for
gene editing. Nature, 555, 435–437. https://doi.org/10.1038/d41586-018-03270-w
Jasanoff, S., & Hurlbut, J. B. (2018a). A global observatory for
gene editing. Nature, 555, 435–437. https://doi.org/10.1038/d41586-018-03270-w
Jasanoff, S., & Kim, S.-H. (Eds.). (2015b). Dreamscapes of
modernity: Sociotechnical imaginaries and the fabrication of power.
https://doi.org/10.7208/chicago/9780226276663.001.0001
Jasanoff, S., & Kim, S.-H. (2015a). Dreamscapes of modernity:
Sociotechnical imaginaries and the fabrication of power. University
of Chicago Press. https://doi.org/10.7208/chicago/9780226276663.001.0001
Jasin, M., & Rothstein, R. (2013). Repair of strand breaks by
homologous recombination. Cold Spring Harbor Perspectives in
Biology, 5(11), a012740. https://doi.org/10.1101/cshperspect.a012740
Ji, R., Chen, Q., & Zhang, Y. (2026). Emerging trends in gene and
cell therapy: CRISPR in DNA editing and
beyond. Cell Reports Medicine, 7(1), 102459. https://doi.org/10.1016/j.xcrm.2025.102459
Ji, Y., Zhou, Z., Liu, H., & Davuluri, R. V. (2021).
DNABERT: Pre-trained bidirectional encoder representations
from transformers model for DNA-language in genome.
Bioinformatics, 37(15), 2112–2120. https://doi.org/10.1093/bioinformatics/btab083
Jiang, K., Yan, Z., Di Bernardo, M., Sgrizzi, S. R., Villiger, L.,
Kayabolen, A., Kim, B. J., Carscadden, J. K., Hiraizumi, M., Nishimasu,
H., Gootenberg, J. S., & Abudayyeh, O. O. (2025). Rapid in silico
directed evolution by a protein language model with
EVOLVEpro. Science, 387(6732), eadr6006.
https://doi.org/10.1126/science.adr6006
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., &
Charpentier, E. (2012). A programmable dual-RNA-guided
DNA endonuclease in adaptive bacterial immunity.
Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M.,
Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žı́dek, A., Potapenko,
A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A.,
Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D.
(2021). Highly accurate protein structure prediction with
AlphaFold. Nature, 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2
Kang, T., Zhang, S., Tang, Y., Hruby, G. W., Rusanov, A., Elhadad, N.,
& Weng, C. (2017). EliIE: An open-source information extraction
system for clinical trial eligibility criteria. Journal of the
American Medical Informatics Association, 24(6),
1062–1071. https://doi.org/10.1093/jamia/ocx019
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., &
Suresh, A. T. (2020). SCAFFOLD: Stochastic controlled
averaging for federated learning. Proceedings of the 37th
International Conference on Machine Learning (ICML), 5132–5143.
Kass, L. R. (2002). Life, liberty and the defense of dignity: The
challenge for bioethics. Encounter Books.
Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., &
Kluger, Y. (2018). DeepSurv: Personalized treatment
recommender system using a Cox proportional hazards deep
neural network. BMC Medical Research Methodology, 18,
24. https://doi.org/10.1186/s12874-018-0482-1
Kay, L. E. (2000). Who wrote the book of life? A
history of the genetic code. Stanford University Press.
Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C.,
Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P.
T., & Kathiresan, S. (2018). Genome-wide polygenic scores for common
diseases identify individuals with risk equivalent to monogenic
mutations. Nature Genetics, 50(9), 1219–1224. https://doi.org/10.1038/s41588-018-0183-z
Kim, H. K., Lee, S., Kim, Y., Park, J., Min, S., Choi, J. W., Huang, T.
P., Yoon, S., Liu, D. R., & Kim, H. H. (2020). High-throughput
analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and
mismatched target sequences in human cells. Nature Biomedical
Engineering, 4(1), 111–124. https://doi.org/10.1038/s41551-019-0505-1
Kim, H. K., Min, S., Song, M., Jung, S., Choi, J. W., Kim, Y., Lee, S.,
Yoon, S., & Kim, H. H. (2019). SpCas9 activity
prediction by DeepSpCas9, a deep learning-based model with
high generalization performance. Science Advances,
5(11), eaax9249. https://doi.org/10.1126/sciadv.aax9249
Kim, H. K., Yu, G., Park, J., Min, S., Lee, S., Yoon, S., & Kim, H.
H. (2021). Predicting the efficiency of prime editing guide
RNAs in human cells. Nature Biotechnology,
39(2), 198–206. https://doi.org/10.1038/s41587-020-0677-y
Kim, H. K., Yu, G., Park, J., Min, S., Lee, S., Yoon, S., & Kim, H.
H. (2023). Predicting the efficiency of prime editing guide
RNAs in human cells. Nature Biotechnology,
41, 1135–1148. https://doi.org/10.1038/s41587-020-0677-y
Klein, E. A., Richards, D., Cohn, A., Tummala, M., Lapham, R., Cosgrove,
D., Chung, G., Clement, J., Gao, J., Hunkapiller, N., Jamshidi, A.,
Kurtzman, K. N., Seiden, M. V., Swanton, C., & Liu, M. C. (2021).
Clinical validation of a targeted methylation-based multi-cancer early
detection test using an independent validation set. Annals of
Oncology, 32(9), 1167–1177. https://doi.org/10.1016/j.annonc.2021.05.806
Kleinstiver, B. P. et al. (2025). Custom
CRISPR-Cas9 PAM variants via
scalable engineering and machine learning. Nature
Biotechnology. https://doi.org/10.1038/s41586-025-09021-y
Klemm, S. L., Shipony, Z., & Greenleaf, W. J. (2019). Chromatin
accessibility and the regulatory epigenome. Nature Reviews
Genetics, 20(4), 207–220. https://doi.org/10.1038/s41576-018-0089-8
Klontzas, M. E., Akinci D’Antonoli, T., Curvo Semedo, L., et al. (2025).
AI medical device post-market surveillance regulations:
Consensus recommendations by the European Society
of Radiology. Insights into Imaging, 16, 275.
https://doi.org/10.1186/s13244-025-02146-8
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A., & Liu, D. R.
(2016). Programmable editing of a target base in genomic
DNA without double-stranded DNA cleavage.
Nature, 533(7603), 420–424. https://doi.org/10.1038/nature17946
Konermann, S., Brigham, M. D., Trevino, A. E., Joung, J., Abudayyeh, O.
O., Barcena, C., Hsu, P. D., Habib, N., Gootenberg, J. S., Nishimasu,
H., Nureki, O., & Zhang, F. (2015). Genome-scale transcriptional
activation by an engineered CRISPR-Cas9
complex. Nature, 517(7536), 583–588. https://doi.org/10.1038/nature14136
Konstantakos, V., Nentidis, A., Krithara, A., & Paliouras, G.
(2022). CRISPRbench: A comprehensive benchmark for
CRISPR-Cas guide RNA efficiency
prediction. Briefings in Bioinformatics, 23(5),
bbac377. https://doi.org/10.1093/bib/bbac377
Labun, K., Montague, T. G., Krause, M., Torres Cleuren, Y. N., Tjeldnes,
H., & Valen, E. (2019). CHOPCHOP v3: Expanding the
CRISPR web toolbox beyond genome editing. Nucleic Acids
Research, 47(W1), W171–W174. https://doi.org/10.1093/nar/gkz365
Lander, E. S., Baylis, F., Zhang, F., Charpentier, E., Berg, P., et al.
(2019). Adopt a moratorium on heritable genome editing. Nature,
567, 165–168. https://doi.org/10.1038/d41586-019-00726-5
Latour, B. (2004). Why has critique run out of steam? From
matters of fact to matters of concern. Critical Inquiry,
30(2), 225–248. https://doi.org/10.1086/421123
Latour, B. (2005). Reassembling the social: An introduction to
Actor-Network-Theory. Oxford University Press.
Laubenbacher, R., Mehrad, B., Shmulevich, I., & Trayanova, N.
(2024). Digital twins in medicine. Nature Computational
Science, 4(3), 184–191. https://doi.org/10.1038/s43588-024-00607-6
Laurent, M., Geoffroy, M., Pavani, G., & Guiraud, S. (2024).
CRISPR-based gene therapies: From preclinical to clinical
treatments. Cells, 13(10), 800. https://doi.org/10.3390/cells13100800
Li, W., Xu, H., Xiao, T., Cong, L., Love, M. I., Zhang, F., Irizarry, R.
A., Liu, J. S., Brown, M., & Liu, X. S. (2014). MAGeCK
enables robust identification of essential genes from genome-scale
CRISPR/Cas9 knockout screens. Genome Biology,
15, 554. https://doi.org/10.1186/s13059-014-0554-4
Lin, J., & Wong, K.-C. (2018). Off-target predictions in
CRISPR-Cas9 gene editing using deep learning.
Bioinformatics, 34(17), i656–i663. https://doi.org/10.1093/bioinformatics/bty554
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N.,
Verkuil, R., Kabeli, O., Shmueli, Y., Santos Costa, A. dos,
Fazel-Zarandi, M., Sercu, T., Candela, S., & Rives, A. (2023).
Evolutionary-scale prediction of atomic-level protein structure with a
language model. Science, 379(6637), 1123–1130. https://doi.org/10.1126/science.ade2574
Lippman, A. (1991). Prenatal genetic testing and screening: Constructing
needs and reinforcing inequities. American Journal of Law &
Medicine, 17(1–2), 15–50. https://doi.org/10.1017/S0098858800007917
Listgarten, J., Weinstein, M., Kleinstiver, B. P., Sousa, A. A., Joung,
J. K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J. G.,
& Fusi, N. (2018). Prediction of off-target activities for the
end-to-end design of CRISPR guide RNAs.
Nature Biomedical Engineering, 2(1), 38–47. https://doi.org/10.1038/s41551-017-0178-6
Liu, J.-J., Orlova, N., Oakes, B. L., Ma, E., Spinner, H. B., Baez, K.
L. M., Duber, J., Schoeb, F., Abudayyeh, O. O., Gootenberg, J. S.,
Zhang, F., Doudna, J. A., & Banfield, J. F. (2019).
CasX enzymes comprise a distinct family of
RNA-guided genome editors. Nature,
566(7743), 218–223. https://doi.org/10.1038/s41586-019-0908-x
Liu, L., Xiong, Y., Zheng, Z., Huang, L., Song, J., Lin, Q., Tang, B.,
& Wong, K.-C. (2024). AutoCancer as an automated multimodal
framework for early cancer detection. iScience, 27(7),
110183. https://doi.org/10.1016/j.isci.2024.110183
Liu, M. C., Oxnard, G. R., Klein, E. A., Swanton, C., & Seiden, M.
V. (2020). Sensitive and specific multi-cancer detection and
localization using methylation signatures in cell-free DNA.
Annals of Oncology, 31(6), 745–759. https://doi.org/10.1016/j.annonc.2020.02.011
London, A. J. (2019). Artificial intelligence and black-box medical
decisions: Accuracy versus explainability. Hastings Center
Report, 49(1), 15–21. https://doi.org/10.1002/hast.973
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Xia, L., et al.
(2023). Predicting cellular responses to complex perturbations in
high-throughput screens. Molecular Systems Biology,
19, e11517. https://doi.org/10.15252/msb.202211517
Low, C. A., Dey, A. K., Ferreira, D., Kamarck, T., Sun, W., Bae, S.,
& Doryab, A. (2020). Behavioral and physiological data from wearable
sensors: Feasibility and acceptability in cancer survivors. PLOS
ONE, 15(9), e0237698. https://doi.org/10.1371/journal.pone.0237698
Lu, N. (2023). Know thy cell-free DNA: Early detection of
microsatellite instability using ultra-low-pass cell-free DNA
sequences [Master’s thesis, Massachusetts Institute of Technology].
https://dspace.mit.edu/handle/1721.1/151965
Luo, Y. et al. (2024). Interpretable CRISPR/Cas9 off-target activities
with mismatches and indels prediction using BERT. Computers in
Biology and Medicine, 169, 107932. https://doi.org/10.1016/j.compbiomed.2024.107932
Lynch, H. T., Snyder, C. L., Shaw, T. G., Heinen, C. D., & Hitchins,
M. P. (2015). Milestones of Lynch syndrome: 1895–2015.
Nature Reviews Cancer, 15(3), 181–194. https://doi.org/10.1038/nrc3878
Mackenzie, C., & Stoljar, N. (Eds.). (2000). Relational
autonomy: Feminist perspectives on autonomy, agency, and the social
self. Oxford University Press.
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P.,
Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., Fraser,
J. S., & Naik, N. (2023). Large language models generate functional
protein sequences across diverse families. Nature
Biotechnology, 41, 1099–1106. https://doi.org/10.1038/s41587-022-01618-2
Makary, M., & Prasad, V. (2025). A new regulatory pathway for
bespoke genetic therapies. New England Journal of Medicine.
Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E.,
Norville, J. E., & Church, G. M. (2013). RNA-guided
human genome engineering via Cas9. Science,
339(6121), 823–826. https://doi.org/10.1126/science.1232033
Martani, A., Weij, F. van der, Baalen, S. van, & Beers, B. van.
(2025). Repoliticizing heritable human genome editing: Discursive
narrowing and technomoral change in the international debate on human
germline modification. New Genetics and Society,
44(1). https://doi.org/10.1080/14636778.2025.2598071
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., &
Daly, M. J. (2019). Clinical use of current polygenic risk scores may
exacerbate health disparities. Nature Genetics, 51(4),
584–591. https://doi.org/10.1038/s41588-019-0379-x
Mathios, D., Johansen, J. S., Cristiano, S., Medina, J. E., Phallen, J.,
Larsen, K. R., Bruhm, D. C., Niknafs, N., Ferreira, L., Adleff, V.,
Chiao, J. Y., Leal, A., Noe, M., White, J. R., Arun, A. S., Hruban, C.,
Annapragada, A. V., Jensen, S. Ø., Ørntoft, M.-B. V., & Velculescu,
V. E. (2021). Detection and characterization of lung cancer using
cell-free DNA fragmentomes. Nature Communications,
12(1), 5060. https://doi.org/10.1038/s41467-021-24994-w
Mavaddat, N., Michailidou, K., Dennis, J., Lush, M., Fachal, L., Lee,
A., Tyrer, J. P., et al. (2019). Polygenic risk scores for prediction of
breast cancer and breast cancer subtypes. American Journal of Human
Genetics, 104(1), 21–34. https://doi.org/10.1016/j.ajhg.2018.11.002
Mazzucato, M. (2013). The entrepreneurial state: Debunking public
vs. Private sector myths. Anthem Press.
Mbembe, A. (2017). Critique of black reason. Duke University
Press. https://doi.org/10.1215/9780822373230
Medical Device Coordination Group. (2025). MDCG 2025-6:
Interplay between the Medical Devices Regulation
(MDR) & In Vitro Diagnostic Medical Devices
Regulation (IVDR) and the Artificial
Intelligence Act (AIA).
Medicines and Healthcare products Regulatory Agency. (2024).
AI airlock: The regulatory sandbox for
AIaMD. GOV.UK. https://www.gov.uk/government/collections/ai-airlock-the-regulatory-sandbox-for-aiamd
Medicines and Healthcare products Regulatory Agency. (2025).
Regulatory framework for point of care manufacturing. GOV.UK.
https://www.gov.uk/government/publications/point-of-care-manufacturing-regulatory-framework
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A.
(2021). Language models enable zero-shot prediction of the effects of
mutations on protein function. Advances in Neural Information
Processing Systems, 34, 29287–29303. https://proceedings.neurips.cc/paper_files/paper/2021/file/f51338d736f95dd42427296047067694-Paper.pdf
Mercieca-Bebber, R., King, M. T., Calvert, M. J., Stockler, M. R., &
Friedlander, M. (2018). The importance of patient-reported outcomes in
clinical trials and strategies for future optimization. Patient
Related Outcome Measures, 9, 353–367. https://doi.org/10.2147/PROM.S156279
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L.
(2016). The ethics of algorithms: Mapping the debate. Big Data &
Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
Mojica, F. J. M., Díez-Villaseñor, C., García-Martínez, J., & Soria,
E. (2005). Intervening sequences of regularly spaced prokaryotic repeats
derive from foreign genetic elements. Journal of Molecular
Evolution, 60(2), 174–182. https://doi.org/10.1007/s00239-004-0046-3
Mol, A. (1999). Ontological politics: A word and some questions. The
Sociological Review, 47(S1), 74–89. https://doi.org/10.1111/j.1467-954X.1999.tb03483.x
Møller, P., Seppälä, T., Bernstein, I., Holinski-Feder, E., Sala, P.,
Evans, D. G., Lindblom, A., Macrae, F., Blanco, I., Jeffries, J., Vasen,
H., Burn, J., Nakken, S., Hovig, E., Rodland, E. A., Tharmaratnam, K.,
Vos Tot Nederveen Cappel, W. H. de, Hill, J., Wijnen, J., et al.others.
(2017). Cancer incidence and survival in lynch syndrome patients
receiving colonoscopic and gynaecological surveillance: First report
from the prospective lynch syndrome database. Gut,
66(3), 464–472. https://doi.org/10.1136/gutjnl-2015-309675
Moreno-Mateos, M. A., Vejnar, C. E., Beaudoin, J.-D., Fernandez, J. P.,
Mis, E. K., Khokha, M. K., & Giraldez, A. J. (2015).
CRISPRscan: Designing highly efficient sgRNAs for CRISPR-Cas9
targeting in vivo. Nature Methods, 12(10), 982–988. https://doi.org/10.1038/nmeth.3543
Musunuru, K., Urnov, F., Giannikopoulos, P., et al. (2025). First
personalised CRISPR therapy administered to an infant with
CPS1 deficiency. Reported by Innovative Genomics
Institute and Children’s Hospital of Philadelphia.
Naldini, L. (2015). Gene therapy returns to centre stage.
Nature, 526(7573), 351–360. https://doi.org/10.1038/nature15818
National Academies of Sciences, Engineering, and Medicine. (2017).
Human genome editing: Science, ethics, and governance. The
National Academies Press. https://doi.org/10.17226/24623
Nayfach, S., Bhatnagar, A., Novichkov, A., Estevam, G. O., Kim, N.,
Hill, E., Ruffolo, J. A., Silverstein, R., Gallagher, J., Kleinstiver,
B., Meeske, A. J., Cameron, P., & Madani, A. (2025). Engineering of
CRISPR-Cas PAM recognition using
deep learning of vast evolutionary data. bioRxiv. https://doi.org/10.1101/2025.01.06.631536
Nelson, J. W., Randolph, P. B., Shen, S. P., Everette, K. A., Chen, P.
J., Anzalone, A. V., An, M., Newby, G. A., Chen, J. C., Hsu, A., &
Liu, D. R. (2022). Engineered pegRNAs
improve prime editing efficiency. Nature Biotechnology,
40(3), 402–410. https://doi.org/10.1038/s41587-021-01039-7
Newby, G. A., Yen, J. S., Woodard, K. J., Mayuranathan, T., Lazzarotto,
C. R., Li, Y., Sheppard-Tillman, H., Porter, S. N., Yao, Y., et al.
(2021). Base editing of haematopoietic stem cells rescues sickle cell
disease in mice. Nature, 595, 295–302. https://doi.org/10.1038/s41586-021-03609-w
Nguengang Wakap, S., Lambert, D. M., Olry, A., Rodwell, C., Gueydan, C.,
Lanneau, V., Murphy, D., Le Cam, Y., & Rath, A. (2020). Estimating
cumulative point prevalence of rare diseases: Analysis of the
Orphanet database. European Journal of Human
Genetics, 28, 165–173. https://doi.org/10.1038/s41431-019-0508-0
Nguyen, E., Poli, M., Durrant, M. G., Thomas, A. W., Kang, B., Sullivan,
J., Ng, M. Y., Lewis, A., Patel, A., Lou, A., Erber, S., et al. (2024).
Sequence modeling and design from molecular to genome scale with
Evo. Science, 386(6723), eado9336. https://doi.org/10.1126/science.ado9336
NHS England. (2025). Revolutionary gene editing therapy for sickle
cell. https://www.england.nhs.uk/2025/01/revolutionary-gene-editing-therapy-for-sickle-cell/.
Ni, Y., Kennebeck, S., Dexheimer, J. W., McAneney, C. M., Tang, H.,
Lingren, T., Li, Q., Zhai, H., & Solti, I. (2015). Automated
clinical trial eligibility prescreening: Increasing the efficiency of
patient identification for clinical trials in the emergency department.
Journal of the American Medical Informatics Association,
22(1), 166–178. https://doi.org/10.1136/amiajnl-2014-002887
Nickel, R. S., Maher, J. Y., Hsieh, M. H., Davis, M. F., Hsieh, M. M.,
& Pecker, L. H. (2022). Fertility after curative therapy for sickle
cell disease: A comprehensive review to guide care. Journal of
Clinical Medicine, 11(9), 2318. https://doi.org/10.3390/jcm11092318
Nishimasu, H., Ran, F. A., Hsu, P. D., Konermann, S., Shehata, S. I.,
Dohmae, N., Ishitani, R., Zhang, F., & Nureki, O. (2014). Crystal
structure of Cas9 in complex with guide RNA
and target DNA. Cell, 156(5), 935–949. https://doi.org/10.1016/j.cell.2014.02.001
Nishimasu, H., Shi, X., Ishiguro, S., Gao, L., Hirano, S., Okazaki, S.,
Noda, T., Abudayyeh, O. O., Gootenberg, J. S., Mori, H., Oura, S.,
Holmes, B., Tanaka, M., Seki, M., Hirano, H., Aburatani, H., Ishitani,
R., Ikawa, M., Yachie, N., … Nureki, O. (2018). Engineered
CRISPR-Cas9 nuclease with expanded targeting
space. Science, 361(6408), 1259–1262. https://doi.org/10.1126/science.aas9129
Niu, B., Ye, K., Zhang, Q., Lu, C., Xie, M., McLellan, M. D., Wendl, M.
C., & Ding, L. (2014). MSIsensor: Microsatellite
instability detection using paired tumor-normal sequence data.
Bioinformatics, 30(7), 1015–1016. https://doi.org/10.1093/bioinformatics/btt755
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A.,
Jost, M., Gilbert, L. A., & Weissman, J. S. (2019). Exploring
genetic interaction manifolds constructed from rich single-cell
phenotypes. Science, 365(6455), 786–793. https://doi.org/10.1126/science.aax4438
Novas, C. (2006). The political economy of hope: Patients’
organizations, science and biovalue. BioSocieties,
1(3), 289–305. https://doi.org/10.1017/S1745855206003024
Nuffield Council on Bioethics. (2018b). Genome editing and human
reproduction: Social and ethical issues.
Nuffield Council on Bioethics. (2018a). Genome editing and human
reproduction: Social and ethical issues. Nuffield Council on
Bioethics. https://www.nuffieldbioethics.org/publications/genome-editing-and-human-reproduction
Nuñez, J. K., Chen, J., Pommier, G. C., Cogan, J. Z., Replogle, J. M.,
Adriaens, C., Ramadoss, G. N., Shi, Q., Hung, K.-L., Samelson, A. J.,
Pogson, A. N., Kim, J. Y. S., Chung, A., Leonetti, M. D., Chang, H. Y.,
Kampmann, M., Bernstein, B. E., Hovestadt, V., Gilbert, L. A., &
Weissman, J. S. (2021). Genome-wide programmable transcriptional memory
by CRISPR-based epigenome editing. Cell,
184(9), 2503–2519. https://doi.org/10.1016/j.cell.2021.03.025
O’Donnell, T. J., Rubinsteyn, A., & Laserson, U. (2020).
MHCflurry 2.0: Improved pan-allele prediction of MHC class I-presented peptides by incorporating
antigen processing. Cell Systems, 11(1), 42–48.e7. https://doi.org/10.1016/j.cels.2020.06.010
Ogden, P. J., Kelsic, E. D., Sinai, S., & Church, G. M. (2019).
Comprehensive AAV capsid fitness landscape reveals a viral
gene and enables machine-guided design. Science,
366(6469), 1139–1143. https://doi.org/10.1126/science.aaw2900
Onnela, J.-P. (2021). Opportunities and challenges in the collection and
analysis of digital phenotyping data. Neuropsychopharmacology,
46(1), 45–54. https://doi.org/10.1038/s41386-020-0771-3
Organizing Committee of the Third International Summit on Human Genome
Editing. (2023). Statement by the organizing committee of the third
international summit on human genome editing. Nature. https://www.nature.com/articles/d41586-023-00747-2
Ottaviano, G., Georgiadis, C., Gkazi, S. A., et al. (2022). Phase 1
clinical trial of CRISPR-engineered CAR19
universal T cells for treatment of children with refractory
B cell leukemia. Science Translational Medicine,
14(668), eabq3010. https://doi.org/10.1126/scitranslmed.abq3010
Pallaseni, A., Peets, E. M., Koeppel, J., Weller, J., Vanderstichele,
T., Ho, U. L., Crepaldi, L., Leeuwen, J. van, Allen, F., & Parts, L.
(2022). Predicting base editing outcomes using position-specific
sequence determinants. Nucleic Acids Research, 50(6),
3551–3564. https://doi.org/10.1093/nar/gkac161
Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight,
L., Hampson, L. V., Holmes, J., Mander, A. P., Odondi, L., Sydes, M. R.,
Villar, S. S., Wason, J. M. S., Weir, C. J., Wheeler, G. M., Yap, C.,
& Jaki, T. (2018). Adaptive designs in clinical trials: Why use
them, and how to run and report them. BMC Medicine,
16, 29. https://doi.org/10.1186/s12916-018-1017-7
Parens, E. (1998). Is better always good? The enhancement
project. In E. Parens (Ed.), Enhancing human traits: Ethical and
social implications (pp. 1–28). Georgetown University Press.
Parens, E., & Asch, A. (2000). The disability rights critique of
prenatal genetic testing: Reflections and recommendations. In E. Parens
& A. Asch (Eds.), Prenatal testing and disability rights
(pp. 3–43). Georgetown University Press.
Parliamentary Assembly of the Council of Europe. (2023). Heritable
genome editing in human beings.
Pausch, P., Al-Shayeb, B., Biber, E., Cress, B. J., Sato, K., Chen, J.,
Banfield, J. F., & Doudna, J. A. (2020).
CRISPR-CasΦ from huge phages is a hypercompact
genome editor. Science, 369(6501), 333–337. https://doi.org/10.1126/science.abb1400
Petryna, A. (2009). When experiments travel: Clinical trials and the
global search for human subjects. Princeton University Press.
Phallen, J., Sausen, M., Adleff, V., Leal, A., Hruban, C., White, J.,
Anagnostou, V., Fiksel, J., Cristiano, S., Papp, E., Speir, S., Reinert,
T., Orntoft, M.-B. W., Woodward, B. D., Murphy, D., Parpart-Li, S.,
Riley, D., Nesselbush, M., Sengamalay, N., … Velculescu, V. E. (2017).
Direct detection of early-stage cancers using circulating tumor
DNA. Science Translational Medicine,
9(403), eaan2415. https://doi.org/10.1126/scitranslmed.aan2415
Picard, M., Scott-Boyer, M.-P., Bodein, A., Pépin, S., & Droit, A.
(2021). Integration strategies of multi-omics data for machine learning
analysis. Computational and Structural Biotechnology Journal,
19, 3735–3746. https://doi.org/10.1016/j.csbj.2021.06.030
Piel, F. B., Steinberg, M. H., & Rees, D. C. (2017). Sickle cell
disease. New England Journal of Medicine, 376(16),
1561–1573. https://doi.org/10.1056/NEJMra1510865
Popejoy, A. B., & Fullerton, S. M. (2016). Genomics is failing on
diversity. Nature, 538(7624), 161–164. https://doi.org/10.1038/538161a
Porter, T. M. (1995). Trust in numbers: The pursuit of objectivity
in science and public life. Princeton University Press.
Prainsack, B., & Buyx, A. (2017). Solidarity in biomedicine and
beyond. Cambridge University Press. https://doi.org/10.1017/9781139696593
President’s Commission for the Study of Ethical Problems in Medicine and
Biomedical and Behavioral Research. (1982). Splicing life: A report
on the social and ethical issues of genetic engineering with human
beings. US Government Printing Office.
Pukkala, E., Engholm, G., Hojsgaard Schmidt, L. K., Storm, H., Khan, S.,
Lambe, M., Pettersson, D., Olafsdottir, E., Tryggvadottir, L., Hakama,
M., Tsnomaki, E., Bray, F., & Klint, Å. (2018). Nordic cancer
registries – an overview of their procedures and data comparability.
Acta Oncologica, 57(4), 440–455. https://doi.org/10.1080/0284186X.2017.1407039
Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S.,
Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., Norris,
A., Sanseau, P., Cavalla, D., & Pirmohamed, M. (2019). Drug
repurposing: Progress, challenges and recommendations. Nature
Reviews Drug Discovery, 18(1), 41–58. https://doi.org/10.1038/nrd.2018.168
Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J.
S., Arkin, A. P., & Lim, W. A. (2013). Repurposing
CRISPR as an RNA-guided platform for
sequence-specific control of gene expression. Cell,
152(5), 1173–1183. https://doi.org/10.1016/j.cell.2013.02.022
Rabeharisoa, V., Moreira, T., & Akrich, M. (2014). Evidence-based
activism: Patients’, users’ and activists’ groups in knowledge society.
In BioSocieties (Vol. 9, pp. 111–128). https://doi.org/10.1057/biosoc.2014.2
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in
medicine. New England Journal of Medicine, 380(14),
1347–1358. https://doi.org/10.1056/NEJMra1814259
Rees, H. A., & Liu, D. R. (2018). Base editing: Precision chemistry
on the genome and transcriptome of living cells. Nature Reviews
Genetics, 19(12), 770–788. https://doi.org/10.1038/s41576-018-0059-1
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A.,
Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K.,
Lithwick-Yanai, G., Bren-Rickson, E., Norman, T. M., & Weissman, J.
S. (2022). Mapping information-rich genotype-phenotype landscapes with
genome-scale Perturb-seq. Cell,
185(14), 2559–2575.e28. https://doi.org/10.1016/j.cell.2022.05.013
Reynisson, B., Alvarez, B., Paul, S., Peters, B., & Nielsen, M.
(2020). NetMHCpan-4.1 and NetMHCIIpan-4.0:
Improved predictions of MHC antigen presentation by
concurrent motif deconvolution and integration of MS
MHC eluted ligand data. Nucleic Acids Research,
48(W1), W449–W454. https://doi.org/10.1093/nar/gkaa379
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J.,
Grody, W. W., Hegde, M., Lyon, E., Spector, E., Voelkerding, K., &
Rehm, H. L. (2015). Standards and guidelines for the interpretation of
sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics
and the Association for Molecular Pathology.
Genetics in Medicine, 17(5), 405–424. https://doi.org/10.1038/gim.2015.30
Richter, M. F., Zhao, K. T., Eton, E., Lapinaite, A., Newby, G. A.,
Thuronyi, B. W., Wilson, C., Koblan, L. W., Zeng, J., Bauer, D. E.,
Doudna, J. A., & Liu, D. R. (2020). Phage-assisted evolution of an
adenine base editor with improved Cas domain compatibility
and activity. Nature Biotechnology, 38(7), 883–891. https://doi.org/10.1038/s41587-020-0453-z
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni,
S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin,
S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., &
Cardoso, M. J. (2020). The future of digital health with federated
learning. Npj Digital Medicine, 3, 119. https://doi.org/10.1038/s41746-020-00323-1
Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018). Deep
generative models of genetic variation capture the effects of mutations.
Nature Methods, 15, 816–822. https://doi.org/10.1038/s41592-018-0138-4
Ringland, G. (1998). Scenario planning: Managing for the
future. John Wiley & Sons.
Rizopoulos, D. (2012). Joint models for longitudinal and
time-to-event data: With applications in R. Chapman;
Hall/CRC.
Roache, R., & Clarke, S. (2009). Bioconservatism, bioliberalism, and
the wisdom of reflecting on repugnance. In Monash bioethics
review (Vol. 28, pp. 1–21). https://doi.org/10.1007/BF03351306
Robertson, J. A. (1994). Children of choice: Freedom and the new
reproductive technologies. Princeton University Press.
Roth, T. L., Puig-Saus, C., Yu, R., Shifrut, E., Carnevale, J., Li, P.
J., Hiatt, J., Saco, J., Krystofinski, P., Li, H., Tobin, V., Nguyen, D.
N., Lee, M. R., Putnam, A. L., Ferris, A. L., Chen, J. W., Schickel,
J.-N., Pellerin, L., Carmody, D., … Marson, A. (2018). Reprogramming
human T cell function and specificity with non-viral genome
targeting. Nature, 559, 405–409. https://doi.org/10.1038/s41586-018-0326-5
Ruffolo, J. A., & Madani, A. (2024). Designing proteins with
language models. Nature Biotechnology, 42, 200–202. https://doi.org/10.1038/s41587-024-02123-4
Salipante, S. J., Scroggins, S. M., Hampel, H. L., Turner, E. H., &
Pritchard, C. C. (2014). Microsatellite instability detection by next
generation sequencing. Clinical Chemistry, 60(9),
1192–1199. https://doi.org/10.1373/clinchem.2014.223677
Sandel, M. J. (2007). The case against perfection: Ethics in the age
of genetic engineering. Harvard University Press.
Savulescu, J. (2005). New breeds of humans: The moral obligation to
enhance. Reproductive BioMedicine Online, 10(Suppl 1),
36–39. https://doi.org/10.1016/S1472-6483(10)62202-X
Savulescu, J., Pugh, J., Douglas, T., & Gyngell, C. (2015). The
moral imperative to continue gene editing research on human embryos.
Protein & Cell, 6(7), 476–479. https://doi.org/10.1007/s13238-015-0184-y
Schambach, A., Buchholz, C. J., Torres-Ruiz, R., Cichutek, K., Morgan,
M., Trapani, I., & Büning, H. (2023). A new age of precision gene
therapy. Lancet, 403(10426), 568–582. https://doi.org/10.1016/S0140-6736(23)01952-9
Schauer, F. (1985). Slippery slopes. Harvard Law Review,
99(2), 361–383. https://doi.org/10.2307/1341127
Scheufele, D. A., Xenos, M. A., Howell, E. L., Rose, K. M., Brossard,
D., & Hardy, B. W. (2017). U.S. Attitudes on human
genome editing. Science, 357(6351), 553–554. https://doi.org/10.1126/science.aan3708
Schmiedel, J. M., & Lehner, B. (2019). Determining protein
structures using deep mutagenesis. Nature Genetics,
51(7), 1177–1186. https://doi.org/10.1038/s41588-019-0431-x
Schwartz, P. (1991). The art of the long view: Planning for the
future in an uncertain world. Doubleday.
Scientific Advice Mechanism High Level Group of Scientific Advisors.
(2018). A scientific perspective on the regulatory status of
products derived from gene editing and the implications for the
GMO directive.
Seale, C., & Gonçalves, J. P. (2025). X-CRISP:
Domain-adaptable and interpretable CRISPR repair outcome
prediction. Bioinformatics Advances, 5(1), vbaf157. https://doi.org/10.1093/bioadv/vbaf157
Shakespeare, T. (2006). Disability rights and wrongs.
Routledge. https://doi.org/10.4324/9780203640098
Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A.,
Mikkelsen, T. S., Heckl, D., Ebert, B. L., Root, D. E., Doench, J. G.,
& Zhang, F. (2014). Genome-scale CRISPR-Cas9 knockout
screening in human cells. Science, 343(6166), 84–87.
https://doi.org/10.1126/science.1247005
Sharma, A., Locatelli, F., Bhatia, M., et al. (2025). Improvements in
health-related quality of life in patients with severe sickle cell
disease after exagamglogene autotemcel. Blood Advances,
9(24), 6481–6490. https://doi.org/10.1182/bloodadvances.2025016701
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S.,
Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., &
Bakas, S. (2020). Federated learning in medicine: Facilitating
multi-institutional collaborations without sharing patient data.
Scientific Reports, 10, 12598. https://doi.org/10.1038/s41598-020-69250-1
Shen, M. W., Arbab, M., Hsu, J. Y., Worstell, D., Culbertson, S. J.,
Krabbe, O., Cassa, C. A., Liu, D. R., Gifford, D. K., & Sherwood, R.
I. (2018). Predictable and precise template-free CRISPR
editing of pathogenic variants. Nature, 563(7733),
646–651. https://doi.org/10.1038/s41586-018-0686-x
Sherkow, J. S. (2017). Patent protection for CRISPR: An
ELSI review. Journal of Law and the Biosciences,
4(3), 565–576. https://doi.org/10.1093/jlb/lsx036
Sherman, R. E., Anderson, S. A., Dal Pan, G. J., Gray, G. W., Gross, T.,
Hunter, N. L., LaVange, L., Marinac-Dabic, D., Marks, P. W., Robb, M.
A., Shuren, J., Temple, R., Woodcock, J., Yue, L. Q., & Califf, R.
M. (2016). Real-world evidence – what is it and what can it tell us?
New England Journal of Medicine, 375(23), 2293–2297.
https://doi.org/10.1056/NEJMsb1609216
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damber, F., Janey, J.
M., Adams, A., & Gonzalez, J. (2021). Bayesian reaction optimization
as a tool for chemical synthesis. Nature, 590, 89–96.
https://doi.org/10.1038/s41586-021-03213-y
Sibieude, E., Khandelwal, A., Girard, P., Hesthaven, J. S., &
Terranova, N. (2022). Population pharmacokinetic model selection
assisted by machine learning. Journal of Pharmacokinetics and
Pharmacodynamics, 49, 257–270. https://doi.org/10.1007/s10928-021-09793-6
Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019). The missing
diversity in human genetic studies. Cell, 177(1),
26–31. https://doi.org/10.1016/j.cell.2019.02.048
Sloot, B. van der, Vetter, A., & Zilgalvis, P. (2024). The
EU Artificial Intelligence Act (2024):
Implications for healthcare. Health Policy, 149,
105173. https://doi.org/10.1016/j.healthpol.2024.105173
Song, M., Kim, H. K., Lee, S., Kim, Y., Seo, S.-Y., Park, J., Choi, J.
W., Jang, H., Shin, J. H., Min, S., Quan, Z., Kim, J. H., Kang, H. C.,
Yoon, S., & Kim, H. H. (2020). Sequence-specific prediction of the
efficiencies of adenine and cytosine base editors. Nature
Biotechnology, 38(9), 1037–1043. https://doi.org/10.1038/s41587-020-0573-5
Sovacool, B. K., & Hess, D. J. (2017). Ordering theories: Typologies
and conceptual frameworks for sociotechnical change. Social Studies
of Science, 47(5), 703–750. https://doi.org/10.1177/0306312717709363
Star, S. L., & Griesemer, J. R. (1989). Institutional ecology,
“translations” and boundary objects: Amateurs and
professionals in Berkeley’s Museum of Vertebrate
Zoology, 1907–39. Social Studies of Science,
19(3), 387–420. https://doi.org/10.1177/030631289019003001
State Council of the People’s Republic of China. (2019).
Administrative regulations on human genetic resources
management.
Stilgoe, J., Owen, R., & Macnaghten, P. (2013a). Developing a
framework for responsible innovation. Research Policy,
42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008
Stilgoe, J., Owen, R., & Macnaghten, P. (2013b). Developing a
framework for responsible research and innovation. Research
Policy, 42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008
Strecker, J., Jones, S., Koopal, B., Schmid-Burgk, J., Zetsche, B., Gao,
L., Makarova, K. S., Koonin, E. V., & Zhang, F. (2019). Engineering
of CRISPR–Cas12b for human genome editing.
Nature Communications, 10, 212. https://doi.org/10.1038/s41467-018-08224-4
Sykora, P., & Caplan, A. (2017). The Council of
Europe should not reaffirm the ban on germline genome editing in
humans. EMBO Reports, 18(11), 1871–1872. https://doi.org/10.15252/embr.201745246
The ASCO Post Staff. (2026). New European project
cluster EARLYSCAN launched to advance early detection of
heritable cancers. https://ascopost.com/news/february-2026/new-european-project-cluster-earlyscan-launched-to-advance-early-detection-of-heritable-cancers/.
Thean, D. G. L., Chu, H. Y., Fong, J. H. C., Chan, B. K. C., Zhou, P.,
Kwok, C. C. S., Chan, Y. M., Mak, S. Y. L., Choi, G. C. G., Ho, J. W.
K., Zheng, Z., & Wong, A. S. L. (2022). Machine learning-coupled
combinatorial mutagenesis enables resource-efficient engineering of
CRISPR-Cas9 genome editor activities.
Nature Communications, 13(1), 2219. https://doi.org/10.1038/s41467-022-29874-5
Thielen, F. W., Meijden, C. M. van der, Meulen, P. van der, et al.
(2022). Towards sustainability and affordability of expensive cell and
gene therapies? Applying a cost-based pricing model to estimate prices
for libmeldy and zolgensma. Cytotherapy, 24(12),
1245–1258. https://doi.org/10.1016/j.jcyt.2022.09.002
Thorlund, K., Dron, L., Park, J. J. H., & Mills, E. J. (2020).
Synthetic and external controls in clinical trials – a primer for
researchers. Clinical Pharmacology & Therapeutics,
107(4), 883–891. https://doi.org/10.1002/cpt.1742
Thorlund, K., Haggstrom, J., Park, J. J. H., & Mills, E. J. (2018).
Key design considerations for adaptive clinical trials: A primer for
clinicians. BMJ, 360, k698. https://doi.org/10.1136/bmj.k698
Timmermans, S., & Berg, M. (2003). The gold standard: The
challenge of evidence-based medicine and standardization in health
care. Temple University Press.
Topol, E. J. (2019). High-performance medicine: The convergence of human
and artificial intelligence. Nature Medicine, 25(1),
44–56. https://doi.org/10.1038/s41591-018-0300-7
Torous, J., Kiang, M. V., Lorme, J., & Onnela, J.-P. (2016). New
tools for new research in psychiatry: A scalable and customizable
platform to empower data driven smartphone research. JMIR Mental
Health, 3(2), e16. https://doi.org/10.2196/mental.5165
Tsai, S. Q., Nguyen, N. T., Malagon-Lopez, J., Topkar, V. V., Aryee, M.
J., & Joung, J. K. (2017). CIRCLE-seq: A highly sensitive in vitro
screen for genome-wide CRISPR–Cas9 nuclease off-targets. Nature
Methods, 14(6), 607–614. https://doi.org/10.1038/nmeth.4278
Tsai, S. Q., Zheng, Z., Nguyen, N. T., Lieber, M., Topkar, V. V.,
Thapar, V., Wyvekens, N., Khaber, C., Iafrate, A. J., Le, L. P., Aryee,
M. J., & Joung, J. K. (2015). GUIDE-seq enables
genome-wide profiling of off-target cleavage by
CRISPR-Cas nucleases. Nature
Biotechnology, 33(2), 187–197. https://doi.org/10.1038/nbt.3117
UK Government. (2015). The human fertilisation and embryology
(mitochondrial donation) regulations 2015. https://www.legislation.gov.uk/uksi/2015/572/contents/made
UK Parliament. (1990). Human fertilisation and embryology act
1990. https://www.legislation.gov.uk/ukpga/1990/37
U.S. Food and Drug Administration. (2018). Framework for
FDA’s real-world evidence program. https://www.fda.gov/media/120060/download.
U.S. Food and Drug Administration. (2019a). Adaptive designs for
clinical trials of drugs and biologics: Guidance for industry. FDA
Guidance Document.
U.S. Food and Drug Administration. (2019b). Proposed regulatory
framework for modifications to artificial intelligence/machine learning
(AI/ML)-based software as a medical device
(SaMD).
U.S. Food and Drug Administration. (2023). FDA approves
first gene therapies to treat sickle cell disease. https://www.fda.gov/news-events/press-announcements/fda-approves-first-gene-therapies-treat-sickle-cell-disease
U.S. Food and Drug Administration. (2024a). Regenerative medicine
advanced therapy designation. https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/regenerative-medicine-advanced-therapy-designation
U.S. Food and Drug Administration. (2024b). Human gene therapy
products incorporating human genome editing. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/human-gene-therapy-products-incorporating-human-genome-editing
VanderWeele, T. J. (2016). Mediation analysis: A practitioner’s guide.
Annual Review of Public Health, 37, 17–32. https://doi.org/10.1146/annurev-publhealth-032315-021402
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C.,
Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., Žı́dek, A.,
Green, T., Tunyasuvunakool, K., Petersen, S., Jumper, J., Clancy, E.,
Green, R., Vora, A., Lutfi, M., … Velankar, S. (2022). AlphaFold
Protein Structure Database: Massively expanding the structural
coverage of protein-sequence space with high-accuracy models.
Nucleic Acids Research, 50(D1), D439–D444. https://doi.org/10.1093/nar/gkab1061
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez,
A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you
need. Advances in Neural Information Processing Systems,
30, 5998–6008. https://doi.org/10.48550/arXiv.1706.03762
Velupillai, S., Suominen, H., Liakata, M., Roberts, A., Shah, A. D.,
Morley, K., Osborn, D., Hayes, J., Stewart, R., Downs, J., Chapman, W.,
& Dutta, R. (2018). Using clinical Natural Language
Processing for health outcomes research: Overview and actionable
suggestions for future advances. Journal of Biomedical
Informatics, 88, 11–19. https://doi.org/10.1016/j.jbi.2018.10.005
Verghese, A., Shah, N. H., & Harrington, R. A. (2018). What this
computer needs is a physician: Humanism and artificial intelligence.
JAMA, 319(1), 19–20. https://doi.org/10.1001/jama.2017.19198
Vertex Pharmaceuticals. (2025). Vertex presents new data on
CASGEVY, including first-ever data in children ages 5–11
years, at the American Society of Hematology Annual
Meeting. https://news.vrtx.com/news-releases/news-release-details/vertex-presents-new-data-casgevyr-including-first-ever-data.
Vertex Pharmaceuticals and CRISPR Therapeutics. (2023).
CASGEVY (exagamglogene
autotemcel) approved by FDA for treatment of sickle
cell disease and transfusion-dependent beta thalassemia. Press
release.
Vertex Pharmaceuticals, & CRISPR Therapeutics. (2023). Vertex
and CRISPR Therapeutics announce US FDA
approval of CASGEVY for the treatment of sickle cell
disease.
Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N.,
Hobbs, B., Ibrahim, J. G., Kinnersley, N., Lindborg, S., Micallef, S.,
Roychoudhury, S., & Thompson, L. (2014). Use of historical control
data for assessing treatment effects in clinical trials.
Pharmaceutical Statistics, 13(1), 41–54. https://doi.org/10.1002/pst.1589
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I.,
Brown, M. A., & Yang, J. (2017). 10 years of GWAS
discovery: Biology, function, and translation. American Journal of
Human Genetics, 101(1), 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005
Vives-Vallés, J. A., & Collonnier, C. (2020). The judgment of the
CJEU of 25 july 2018 on mutagenesis: Interpretation and
interim legislative proposal. Frontiers in Plant Science,
10, 1813. https://doi.org/10.3389/fpls.2019.01813
Vojta, A., Dobrinić, P., Tadić, V., Bočkor, L., Korać, P., Julg, B.,
Klasić, M., & Zoldoš, V. (2016). Repurposing the
CRISPR-Cas9 system for targeted
DNA methylation. Nucleic Acids Research,
44(12), 5615–5628. https://doi.org/10.1093/nar/gkw159
Wagner, D. L., Amini, L., Wendering, D. J., Burkhardt, L.-M., Akyüz, L.,
Reinke, P., Volk, H.-D., & Schweizer, M. (2019). High prevalence of
Streptococcus pyogenes Cas9-reactive
T cells within the adult human population. Nature
Medicine, 25(2), 242–248. https://doi.org/10.1038/s41591-018-0204-6
Walton, R. T., Christie, K. A., Whittaker, M. N., & Kleinstiver, B.
P. (2020). Unconstrained genome targeting with near-PAMless
engineered CRISPR-Cas9 variants.
Science, 368(6488), eaba8853. https://doi.org/10.1126/science.aba8853
Wan, J. C. M., Massie, C., Garcia-Corbacho, J., Mouliere, F., Brenton,
J. D., Caldas, C., Pacey, S., Baird, R., & Rosenfeld, N. (2017).
Liquid biopsies come of age: Towards implementation of circulating
tumour DNA. Nature Reviews Cancer, 17(4),
223–238. https://doi.org/10.1038/nrc.2017.7
Wang, D., Zhang, F., & Gao, G. (2020). CRISPR-based
therapeutic genome editing: Strategies and in vivo delivery by
AAV vectors. Cell, 181(1), 136–150. https://doi.org/10.1016/j.cell.2020.03.023
Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014).
Genetic screens in human cells using the
CRISPR-Cas9 system. Science,
343(6166), 80–84. https://doi.org/10.1126/science.1246981
Wang, Y., Sohn, S., & Liu, H. (2022). CancerBERT: A cancer
domain-specific language model for extracting cancer phenotypes from
clinical text. Journal of Biomedical Informatics, 130,
104072. https://doi.org/10.1016/j.jbi.2022.104072
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N.,
Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., & Liu, H. (2018). Clinical
information extraction applications: A literature review. Journal of
Biomedical Informatics, 77, 34–49. https://doi.org/10.1016/j.jbi.2017.11.011
Wasmer, M. (2019). Roads forward for european GMO policy —
uncertainties in wake of ECJ judgment have to be mitigated
by regulatory reform. Frontiers in Bioengineering and
Biotechnology, 7, 132. https://doi.org/10.3389/fbioe.2019.00132
Wienert, B., Wyman, S. K., Richardson, C. D., Yeh, C. D., Akcakaya, P.,
Porritt, M. J., Morlock, M., Vu, J. T., Kazane, K. R., Watry, H. L.,
Judge, L. M., Conklin, B. R., Maresca, M., & Corn, J. E. (2019).
Unbiased detection of CRISPR off-targets in vivo using
DISCOVER-seq. Science, 364(6437),
286–289. https://doi.org/10.1126/science.aav9023
Wilkinson, A., & Kupers, R. (2014). The essence of scenarios:
Learning from the shell experience. Amsterdam University Press. https://doi.org/10.5117/9789089646927
Winner, L. (1980). Do artifacts have politics? Daedalus,
109(1), 121–136. https://www.jstor.org/stable/20024652
Wishart, D. S. (2019). Metabolomics for investigating physiological and
pathophysiological processes. Physiological Reviews,
99(4), 1819–1875. https://doi.org/10.1152/physrev.00035.2018
World Health Organization. (2021b). Human genome editing: A
framework for governance. https://www.who.int/publications/i/item/9789240030060
World Health Organization. (2021a). Human genome editing: A
framework for governance. World Health Organization.
World Health Organization. (2021c). Human genome editing:
recommendations. https://www.who.int/publications/i/item/9789240030381
Wu, W., Yang, J., Tan, Y., Gu, K., Shen, Q., Yang, C., Hu, M., Xiang,
Y., & Xu, W. (2025). Cost-effectiveness of colorectal cancer
screening under different scenarios of colonoscopy adherence: A
microsimulation study. BMJ Public Health, 3(1),
e001344. https://doi.org/10.1136/bmjph-2024-001344
Wu, Y., Zeng, J., Roscoe, B. P., et al. (2019). Highly efficient
therapeutic gene editing of human hematopoietic stem cells. Nature
Medicine, 25(5), 776–783. https://doi.org/10.1038/s41591-019-0401-y
Yeh, C. D., Richardson, C. D., & Corn, J. E. (2019). Advances in
genome editing through control of DNA repair pathways.
Nature Cell Biology, 21(12), 1468–1478. https://doi.org/10.1038/s41556-019-0425-z
Yoon, J., Kim, J., Park, S., Lee, M., et al. (2025). Brief summary of
the regulatory frameworks of regenerative medicine therapies.
Frontiers in Pharmacology, 15, 1486812. https://doi.org/10.3389/fphar.2024.1486812
Yotova, R. (2020). Regulating genome editing under international human
rights law. International and Comparative Law Quarterly,
69(3), 653–684. https://doi.org/10.1017/S0020589320000184
Young, I. M. (2011). Responsibility for justice. Oxford
University Press. https://doi.org/10.1093/acprof:oso/9780195392388.001.0001
Zeng, S., Liu, L. J., Wen, J., Yetisgen, M., Etzioni, R., & Luo, G.
(2025). TrajSurv: Learning continuous latent trajectories from
electronic health records for trustworthy survival prediction.
Proceedings of Machine Learning for Healthcare, 298,
1–27.
Zetsche, B., Gootenberg, J. S., Abudayyeh, O. O., Slaymaker, I. M.,
Makarova, K. S., Essletzbichler, P., Volz, S. E., Joung, J., Oost, J.
van der, Regev, A., Koonin, E. V., & Zhang, F. (2015).
Cpf1 is a single RNA-guided endonuclease of a
class 2 CRISPR-Cas system. Cell,
163(3), 759–771. https://doi.org/10.1016/j.cell.2015.09.038
Zhang, G., Dai, Z., & Dai, X. (2020). CRISPR-IP: A
CRISPR off-target prediction tool with improved
performance. Bioinformatics, 36(10), 3137–3138. https://doi.org/10.1093/bioinformatics/btaa143
Zhang, Y., Long, Y., & Kwoh, C. K. (2023).
CRISPR-BERT: Enhancing CRISPR/Cas9
off-target activities with mismatches and indels prediction using
BERT-based convolutional and recurrent neural
networks. https://github.com/BrokenStringx/CRISPR-BERT.
Zhu, H., Richmond, E., & Liang, C. (2019).
CRISPR-based tools for multiplex genome editing.
Methods in Molecular Biology, 1961, 53–67. https://doi.org/10.1007/978-1-4939-9170-9_4