Martina Elena Tarozzi Science Reviews - Biology, 2024, 3(1), 9-15
18. Gu F, Wang X. Analysis of allele specific expression-a survey. Tsinghua Sci. Technol. 2015;
20:513–529
19. Im YR, Tsui DWY, Diaz LA, et al. Next-Generation Liquid Biopsies: Embracing Data Science in
Oncology. Trends in Cancer 2021; 7:283–292
20. Zhou J, Li L, Wang L, et al. Establishment of a SVM classifier to predict recurrence of ovarian
cancer. Mol. Med. Rep. 2018; 18:3589–3598
21. Xu G, Zhang M, Zhu H, et al. A 15-gene signature for prediction of colon cancer recurrence and
prognosis based on SVM. Gene 2017; 604:33–40
22. Constantin N, Sina AAI, Korbie D, et al. Opportunities for Early Cancer Detection: The Rise of
ctDNA Methylation-Based Pan-Cancer Screening Technologies. Epigenomes 2022; 6:1–27
23. Bahado-Singh RO, Radhakrishna U, Gordevičius J, et al. Artificial Intelligence and Circulating
Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer’s Disease. Cells 2022;
11:1–19
24. Kelley DR, Snoek J, Rinn JL. Basset: Learning the regulatory code of the accessible genome with
deep convolutional neural networks. Genome Res. 2016; 26:990–999
25. Onimaru K, Nishimura O, Kuraku S. Predicting gene regulatory regions with a convolutional
neural network for processing double-strand genome sequence information. PLoS One 2020;
15:e0235748
26. Kelley DR, Reshef YA, Bileschi M, et al. Sequential regulatory activity prediction across
chromosomes with convolutional neural networks. 2018;
27. Fudenberg G, Kelley DR, Pollard KS. Predicting 3D genome folding from DNA sequence with
Akita.
28. Jinek M, Chylinski K, Fonfara I, et al. A programmable dual-RNA-guided DNA endonuclease in
adaptive bacterial immunity. Science (80-. ). 2012; 337:816–821
29. Doench JG, Hartenian E, Graham DB, et al. Rational design of highly active sgRNAs for CRISPR-
Cas9–mediated gene inactivation. Nat. Biotechnol. 2014 3212 2014; 32:1262–1267
30. Doench JG, Fusi N, Sullender M, et al. Optimized sgRNA design to maximize activity and
minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 2015 342 2016; 34:184–191
31. Kim HK, Kim Y, Lee S, et al. SpCas9 activity prediction by DeepSpCas9, a deep learning–based
model with high generalization performance. Sci. Adv. 2019; 5:
32. Chuai G, Ma H, Yan J, et al. DeepCRISPR: Optimized CRISPR guide RNA design by deep
learning. Genome Biol. 2018; 19:1–18
33. Kim HK, Min S, Song M, et al. Deep learning improves prediction of CRISPR–Cpf1 guide RNA
activity. Nat. Biotechnol. 2018 363 2018; 36:239–241
34. Moreno-Mateos MA, Vejnar CE, Beaudoin JD, et al. CRISPRscan: designing highly efficient
sgRNAs for CRISPR-Cas9 targeting in vivo. Nat. Methods 2015 1210 2015; 12:982–988
35. Wang J, Zhang X, Cheng L, et al. An overview and metanalysis of machine and deep learning-
based CRISPR gRNA design tools. RNA Biol. 2020; 17:13