HsuPD, LanderES, ZhangF. Development and applications of CRISPR-Cas9 for genome engineering. Cell, 2014; 157:1262–1278. DOI: 10.1016/j.cell.2014.05.010.
2.
FellmannC, GowenB, LinPC, et al.Cornerstones of CRISPR-Cas in drug discovery and therapy. Nat Rev Drug Discov, 2017; 16:89–100. DOI: 10.1038/nrd.2016.238.
3.
TasanI, ZhaoH. Targeting specificity of the CRISPR/Cas9 system. ACS Synth Biol, 2017; 6:1609–1613. DOI: 10.1021/acssynbio.7b00270.
4.
KimN, KimHK, LeeS, et al.Prediction of the sequence-specific cleavage activity of Cas9 variants. Nat Biotechnol, 2020; 38:1328–1336. DOI: 10.1038/s41587-020-0537-9.
5.
KimH, SongM, LeeJ, et al.In vivo high-throughput profiling of CRISPR–Cpf1 activity. Nat Methods, 2017; 14:153–159. DOI: 10.1038/nmeth.4104.
6.
VolkMJ, LourentzouI, MishraS, et al.Biosystems design by machine learning. ACS Synth Biol, 2020; 9:1514–1533. DOI: 10.1021/acssynbio.0c00129.
7.
ZhouJ, TroyanskayaOG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods, 2015; 12:931–934. DOI: 10.1038/nmeth.3547.
8.
KimH, MinS, SongM, et al.Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nat Biotech, 2018; 36:239–241. DOI: 10.1038/nbt.4061.
9.
KimHK, KimY, LeeS, et al.SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Sci Adv, 2019; 5:eaax9249. DOI: 10.1126/sciadv.aax9249.
10.
RaoR, BhattacharyaN, ThomasN, et al.Evaluating protein transfer learning with TAPE. Adv Neural Inf Process Syst, 2019; 32:9686–9701. DOI: 10.1101/676825.
11.
BiswasS, KhimulyaG, AlleyEC, et al.Low-N protein engineering with data-efficient deep learning. biorxiv, 2020. DOI: 10.1101/2020.01.23.917682v2.
12.
AzodiCB, TangJ, ShiuS-H. Opening the black box: interpretable machine learning for geneticists. Trends Genet, 2020; 36:442–455. DOI: 10.1016/j.tig.2020.03.005.
13.
KimGB, GaoY, PalssonBO, et al.DeepTFactor: a deep learning-based tool for the prediction of transcription factors. Proc Natl Acad Sci U S A, 2021; 118:e2021171118. DOI: 10.1073/pnas.2021171118.