Emerging applications of machine learning in genomic medicine and healthcare

References

• Leung MK, Delong A, Alipanahi B, et al. Machine learning in genomic medicine: a review of computational problems and data sets. Proc IEEE. 2016;104(1):176–197. doi: 10.1109/JPROC.2015.2494198.
   Web of Science ®Google Scholar
• Hayes DF, Anne FS. Personalized medicine: genomics trials in oncology. Transac Am Clin Climatol Assoc. 2015;126:133–143. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4530676/
   PubMedGoogle Scholar
• Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016;15(7):473–484. doi: 10.1038/nrd.2016.32.
   PubMed Web of Science ®Google Scholar
• Parikh VN, Ashley EA. Next-Generation sequencing in cardiovascular disease. Circulation. 2017;135(5):406–409. doi: 10.1161/CIRCULATIONAHA.116.024258.
   PubMed Web of Science ®Google Scholar
• Berkowitz CL, Mosconi L, Rahman A, et al. Clinical application of APOE in alzheimer’s prevention: a precision medicine approach. J Prev Alzheimers Dis. 2018;5(4):245–252. doi: 10.14283/jpad.2018.35.
   PubMed Web of Science ®Google Scholar
• Zeggini E, Gloyn AL, Barton AC, et al. Translational genomics and precision medicine: moving from the lab to the clinic. Science. 2019;365(6460):1409–1413. doi: 10.1126/science.aax4588.
   PubMed Web of Science ®Google Scholar
• Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321–332. doi: 10.1038/nrg3920.
   PubMed Web of Science ®Google Scholar
• El Naqa I, Murphy MJ. What is machine learning? In: I. El Naqa, R. Li, and MJ. Murphy, editors. Machine learning in radiation oncology: theory and applications. Cham: springer International Publishing; 2015. pp. 3–11. doi: 10.1007/978-3-319-18305-3_1.
   Google Scholar
• Kang M, Jameson NJ. Machine learning: fundamentals. In: MG. Pecht and M. Kang, editors. Prognostics and health management of electronics. Chichester: John Wiley and Sons Ltd.; 2018. pp. 85–109. doi: 10.1002/9781119515326.ch4.
   Google Scholar
• Eraslan G, Avsec Ž, Gagneur J, et al. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20(7):389–403. doi: 10.1038/s41576-019-0122-6.
   PubMed Web of Science ®Google Scholar
• Hassan M, Awan FM, Naz A, et al. Innovations in genomics and big data analytics for personalized medicine and health care: a review. Int J Mol Sci. 2022;23(9):4645. doi: 10.3390/ijms23094645.
   PubMed Web of Science ®Google Scholar
• Erhan D, Courville A, Bengio Y, et al. Why does unsupervised pre-training help deep learning? In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2010. pp. 201–208.
   Google Scholar
• Raina R, Battle A, Lee H, et al. Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th international conference on Machine learning. 2007. pp. 759–766. doi: 10.1145/1273496.1273592.
   Google Scholar
• Zhang R, Isola P, Efros AA, et al. The unreasonable effectiveness of deep features as a perceptual metric In Proceedings of the IEEE conference on computer vision and pattern recognition. 2018;p. 586–595.
   Google Scholar
• Maulud D, Abdulazeez AM. A review on linear regression comprehensive in machine learning. JASTT. 2020;1(4):140–147. doi: 10.38094/jastt1457.
   Google Scholar
• LaValley MP. Logistic regression. Circulation. 2008;117(18):2395–2399. doi: 10.1161/CIRCULATIONAHA.106.682658.
   PubMed Web of Science ®Google Scholar
• Nick TG, Campbell KM. Logistic regression. Topics in biostatistics. Methods Mol Biol. 2007;404:273–301. doi: 10.1007/978-1-59745-530-5_14.
   PubMedGoogle Scholar
• Walker DA, Smith TJ. Nine pseudo R2 indices for binary logistic regression models. J Mod App Stat Meth. 2016;15(1):848–854. doi: 10.22237/jmasm/1462077720.
   Web of Science ®Google Scholar
• Zou X, Hu Y, Tian Z, et al. Logistic regression model optimization and case analysis. In: 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). 2019. pp. 135–39. IEEE. doi: 10.1109/ICCSNT47585.2019.8962457.
   Google Scholar
• Gupta B, Rawat A, Jain A, et al. Analyse of various decision tree algorithms for classification in data mining. IJCA. 2017;163(8):15–19. doi: 10.5120/ijca2017913660.
   Google Scholar
• Rohde PD, Sørensen IF, Sørensen P. Expanded utility of the R package qgg with applications within genomic medicine. bioRxiv. 2022.
   Google Scholar
• Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123–140. doi: 10.1007/BF00058655.
   Web of Science ®Google Scholar
• Bühlmann P. Bagging, boosting and ensemble methods. Handb Comput Stat. 2012. pp. 985–1022. doi: 10.1007/978-3-642-21551-3_33.
   Google Scholar
• Frank E, Pfahringer B. Improving on bagging with input smearing. In: Ng. WK, Kitsuregawa. M, Li. J, Chang. K, editors. Advances in knowledge discovery and data mining. PAKDD 2006. Lecture Notes in Computer Science, vol 3918; Berlin, Heidelberg: Springer; 2006. pp. 97–106. doi: 10.1007/11731139_14.
   Google Scholar
• Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Kingsford C, Salzberg SL. What are decision trees? Nat Biotechnol. 2008;26(9):1011–1013. doi: 10.1038/nbt0908-1011.
   PubMed Web of Science ®Google Scholar
• Freund Y, Schapire RE. Experiments with a new boosting algorithm. Mach Learn. 1996;96(7):148–156.
   Google Scholar
• Mayr A, Binder H, Gefeller O, et al. The evolution of boosting algorithms. Methods Inf Med. 2014;53(6):419–427. doi: 10.3414/ME13-01-0122.
   PubMed Web of Science ®Google Scholar
• Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Statist. 2001;29(5):1189–1232. https://www.jstor.org/stable/2699986. doi: 10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• González S, García S, Del Ser J, et al. A practical tutorial on bagging and boosting based ensembles for machine learning: algorithms, software tools, performance study, practical perspectives and opportunities. Information Fusion. 2020;64(12):205–237. doi: 10.1016/j.inffus.2020.07.007.
   Google Scholar
• Huang HH, Liu XY, Liang Y. Feature selection and cancer classification via sparse logistic regression with the hybrid L1/2 + 2 regularization. PLoS One. 2016;11(5):e0149675. doi: 10.1371/journal.pone.0149675.
   PubMed Web of Science ®Google Scholar
• Patle A, Chouhan DS. SVM kernel functions for classification. In: 2013 International Conference on Advances in Technology and Engineering (ICATE). 2013. pp. 1–9. IEEE. doi: 10.1109/ICAdTE.2013.6524743.
   Google Scholar
• Ozer ME, Sarica PO, Arga KY. New machine learning applications to accelerate personalized medicine in breast cancer: rise of the support vector machines. OMICS. 2020;24(5):241–246. doi: 10.1089/omi.2020.0001.
   PubMed Web of Science ®Google Scholar
• Zhang Z. Introduction to machine learning: k -Nearest neighbors. Ann Transl Med. 2016;4(11):218–218. doi: 10.21037/atm.2016.03.37.
   PubMed Web of Science ®Google Scholar
• James G, Witten D, Hastie T, et al. Unsupervised learning. In: G. James, D Witten, T. Hastie and R. Tibshirani, editors. An introduction to statistical learning: with applications in R, Springer Texts in Statistics. New York (NY): Springer US; 2021. pp. 497–552. doi: 10.1007/978-1-0716-1418-1_12.
   Google Scholar
• Hinton GE, Roweis S. Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems. 2002.
   Google Scholar
• Hozumi Y, Wang R, Yin C, et al. Umap: uniform manifold approximation and projection for dimension reduction. arXiv Preprint arXiv. 2020.
   Google Scholar
• Hansen LK, Larsen J. Unsupervised learning and generalization. In: Proceedings of. International Conference on Neural Networks (ICNN’96), Washington, DC, USA: IEEE. 1996. pp. 25–30. doi: 10.1109/ICNN.1996.548861.
   Google Scholar
• Singh K, Malik D, Sharma N. Evolving limitations in K-means algorithm in data mining and their removal. Inter J Comput Engin Manage. 2011;12(1):105–109.
   Google Scholar
• Sinaga KP, Yang MS. Unsupervised K-Means clustering algorithm. IEEE Access. 2020;8:80716–80727. doi: 10.1109/ACCESS.2020.2988796.
   Google Scholar
• Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90(430):773–795. doi: 10.1080/01621459.1995.10476572.
   Web of Science ®Google Scholar
• Bozdogan H. Model selection and akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika. 1987;52(3):345–370. doi: 10.1007/BF02294361.
   Web of Science ®Google Scholar
• Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20(11):53–65. doi: 10.1016/0377-0427(87)90125-7.
   Google Scholar
• Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact Well-Separated clusters. Journal of Cybernetics. 1973;3(3):32–57. doi: 10.1080/01969727308546046.
   Google Scholar
• Calinski T, Harabasz J. A dendrite method for cluster analysis. Comm in Stats Theory Methods. 1974;3(1):1–27. doi: 10.1080/03610927408827101.
   Google Scholar
• Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell. 1979;PAMI-1(2):224–227. doi: 10.1109/TPAMI.1979.4766909.
   Web of Science ®Google Scholar
• Ravì D, Wong C, Deligianni F, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21. doi: 10.1109/JBHI.2016.2636665.
   PubMed Web of Science ®Google Scholar
• Shrestha A, Mahmood A. Review of deep learning algorithms and architectures. IEEE Access. 2019;7:53040–53065. doi: 10.1109/ACCESS.2019.2912200.
   Google Scholar
• Buscema M. Back propagation neural networks. Subst Use Misuse. 1998;33(2):233–270. doi: 10.3109/10826089809115863.
   PubMed Web of Science ®Google Scholar
• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi: 10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Li Y, Huang C, Ding L, et al. Deep learning in bioinformatics: introduction, application, and perspective in the big data era. Methods. 2019;166(8):4–21. doi: 10.1016/j.ymeth.2019.04.008
   PubMedGoogle Scholar
• Pereira FC, Borysov SS. Chapter 2 - Machine learning fundamentals. Mobility patterns, big data and transport analytics. Amsterdam, Netherlands: Elsevier; 2019. pp. 9–29. doi: 10.1016/B978-0-12-812970-8.00002-6.
   Google Scholar
• Sapoval N, Aghazadeh A, Nute MG, et al. Current progress and open challenges for applying deep learning across the biosciences. Nat Commun. 2022;13(1):1728. doi: 10.1038/s41467-022-29268-7.
   PubMed Web of Science ®Google Scholar
• Montavon G, Samek W, Müller KR. Methods for interpreting and understanding deep neural networks. Digital Signal Process. 2018;73:1–15. doi: 10.1016/j.dsp.2017.10.011.
   Web of Science ®Google Scholar
• Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589. doi: 10.1038/s41586-021-03819-2.
   PubMed Web of Science ®Google Scholar
• Berman HM, Battistuz T, Bhat TN, et al. The protein data bank. Acta Crystallogr D Biol Crystallogr. 2002;58(Pt 6 No 1):899–907. doi: 10.1107/S0907444902003451.
   PubMedGoogle Scholar
• Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11(1):70. doi: 10.1186/s13073-019-0689-8.
   PubMed Web of Science ®Google Scholar
• Abd-Alrazaq A, Alajlani M, Alhuwail D, et al. Artificial intelligence in the fight against COVID-19: scoping review. J Med Internet Res. 2020;22(12):e20756. doi: 10.2196/20756.
   PubMed Web of Science ®Google Scholar
• Ellahham S. Artificial intelligence: the future for diabetes care. Am J Med. 2020;133(8):895–900. doi: 10.1016/j.amjmed.2020.03.033.
   PubMed Web of Science ®Google Scholar
• Iqbal MJ, Javed Z, Sadia H, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int. 2021;21(1):270. doi: 10.1186/s12935-021-01981-1.
   PubMed Web of Science ®Google Scholar
• Poplin R, Chang PC, Alexander D, et al. A universal SNP and Small-Indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983–987. doi: 10.1038/nbt.4235.
   PubMed Web of Science ®Google Scholar
• Fabrizio C, Termine A, Caltagirone C, et al. Artificial intelligence for alzheimer’s disease: promise or challenge? Diagnostics (Basel). 2021;11(8):1473. doi: 10.3390/diagnostics11081473.
   PubMedGoogle Scholar
• Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42(12):60–88. doi: 10.1016/j.media.2017.07.005.
   PubMedGoogle Scholar
• Salehi AW, Baglat P, Sharma BB, et al. A CNN model: earlier diagnosis and classification of alzheimer disease using MRI. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC). 2020. pp. 156–161. doi: 10.1109/ICOSEC49089.2020.9215402.
   Google Scholar
• Qin C, Yao D, Shi Y, et al. Computer-Aided detection in chest radiography based on artificial intelligence: a survey. BioMed Eng OnLine. 2018;17(1):113. doi: 10.1186/s12938-018-0544-y.
   PubMedGoogle Scholar
• Esteva A, Kuprel B, Novoa BA, et al. Dermatologist-Level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi: 10.1038/nature21056.
   PubMed Web of Science ®Google Scholar
• Gao Y, Geras KJ, Lewin AA, et al. New frontiers: an update on Computer-Aided diagnosis for breast imaging in the age of artificial intelligence. AJR Am J Roentgenol. 2019;212(2):300–307. doi: 10.2214/AJR.18.20392.
   PubMed Web of Science ®Google Scholar
• Yao JC, Hou GH, Ou D, et al. AI detection of mild COVID-19 pneumonia from chest CT scans. Eur Radiol. 2021;31(9):7192–7201. doi: 10.1007/s00330-021-07797-x.
   PubMed Web of Science ®Google Scholar
• Zebari DA, Ibrahim DA, Zeebaree DQ, et al. Systematic review of computing approaches for breast cancer detection based Computer-Aided diagnosis using mammogram images. Appl Artif Intel. 2021;35(15):2157–2203. doi: 10.1080/08839514.2021.2001177.
   Web of Science ®Google Scholar
• Li K, Liu K, Zhong Y, et al. Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system. Quant Imaging Med Surg. 2021;11(8):3629–3642. doi: 10.21037/qims-20-1314.
   PubMed Web of Science ®Google Scholar
• Ibrahim AU, Al-Turjman F, Ozsoz M, et al. Computer aided detection of tuberculosis using two classifiers. Biomed Tech (Berl). 2022;67(6):513–524. doi: 10.1515/bmt-2021-0310.
   PubMed Web of Science ®Google Scholar
• Li L, Cheng WY, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311):311ra174. doi: 10.1126/scitranslmed.aaa9364.
   PubMed Web of Science ®Google Scholar
• Yuan KC, Tsai LW, Lee KH, et al. The development of an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int J Med Inform. 2020;141:104176. doi: 10.1016/j.ijmedinf.2020.104176.
   PubMed Web of Science ®Google Scholar
• Villar JR, González S, Sedano J, et al. Improving human activity recognition and its application in early stroke diagnosis. Int J Neural Syst. 2015;25(4):1450036. doi: 10.1142/S0129065714500361.
   PubMed Web of Science ®Google Scholar
• Gupta R, Srivastava D, Sahu M, et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25(3):1315–1360. doi: 10.1007/s11030-021-10217-3.
   PubMed Web of Science ®Google Scholar
• Che J, Feng R, Gao J, et al. Evaluation of artificial intelligence in participating Structure-Based virtual screening for identifying novel interleukin-1 receptor associated kinase-1 inhibitors. Front Oncol. 2020;10:1769. 10.3389/fonc.2020.01769.
   PubMed Web of Science ®Google Scholar
• Hessler G, Baringhaus KH. Artificial intelligence in drug design. Molecules. 2018;23(10):2520. doi: 10.3390/molecules23102520.
   PubMed Web of Science ®Google Scholar
• Koromina M, Pandi MT, Patrinos GP. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. OMICS. 2019;23(11):539–548. doi: 10.1089/omi.2019.0151.
   PubMed Web of Science ®Google Scholar
• Sellwood MA, Ahmed M, Segler MH, et al. Artificial intelligence in drug discovery. Future Med Chem. 2018;10(17):2025–2028. doi: 10.4155/fmc-2018-0212.
   PubMed Web of Science ®Google Scholar
• Obrezanova O. Artificial intelligence for compound pharmacokinetics prediction. Curr Opin Struct Biol. 2023;79(4):102546. doi: 10.1016/j.sbi.2023.102546.
   PubMedGoogle Scholar
• Bian Y, Xie XQ. Generative chemistry: drug discovery with deep learning generative models. J Mol Model. 2021;27(3):71. doi: 10.1007/s00894-021-04674-8.
   PubMed Web of Science ®Google Scholar
• Keshavarzi Arshadi A, Webb J, Salem M, et al. Artificial intelligence for COVID-19 drug discovery and vaccine development. Front Artif Intell. 2020;3:65. 10.3389/frai.2020.00065.
   PubMedGoogle Scholar
• Lv H, Shi L, Berkenpas JW, et al. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Brief Bioinform. 2021;22(6):bbab320. doi: 10.1093/bib/bbab320.
   PubMed Web of Science ®Google Scholar
• Zhang H, Saravanan KM, Yang Y, et al. Deep learning based drug screening for novel coronavirus 2019-NCov. Interdiscip Sci. 2020;12(3):368–376. doi: 10.1007/s12539-020-00376-6.
   PubMed Web of Science ®Google Scholar
• Ge Y, Tian T, Huang S, et al. An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. Sig Transduct Target Ther. 2021;6(1):165. doi: 10.1038/s41392-021-00568-6.
   PubMed Web of Science ®Google Scholar
• Morselli G, Ítalo do Valle D, Zitnik M, et al. Network medicine framework for identifying Drug-Repurposing opportunities for COVID-19. Proc Natl Acad Sci USA. 2021;118(19):e2025581118.): doi: 10.1073/pnas.2025581118.
   PubMed Web of Science ®Google Scholar
• Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol. 2022;39(8):120. doi: 10.1007/s12032-022-01711-1.
   PubMed Web of Science ®Google Scholar
• Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–1930. doi: 10.1161/CIRCULATIONAHA.115.001593.
   PubMed Web of Science ®Google Scholar
• Tadist K, Najah S, Nikolov NS, et al. Feature selection methods and genomic big data: a systematic review. J Big Data. 2019;6(1):79. doi: 10.1186/s40537-019-0241-0.
   Google Scholar
• Soumare H, Benkahla A, Gmati N. Deep learning regularization techniques to genomics data. Array. 2021;11(9):100068. doi: 10.1016/j.array.2021.100068.
   Google Scholar
• Bergstra J, Bardenet R, Bengio Y, et al. Algorithms for Hyper-Parameter optimization. In: Advances in Neural Information Processing Systems. 2011. https://proceedings.neurips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html
   Google Scholar
• Alibrahim H, Ludwig SA. Hyperparameter optimization: comparing genetic algorithm against grid search and bayesian optimization. In: 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland: IEEE. 2021. pp. 1551–1559. doi: 10.1109/CEC45853.2021.9504761.
   Google Scholar
• Wu J, Chen XY, Zhang H, et al. Hyperparameter optimization for machine learning models based on Bayesian. J Optimiz Electronic Sci Technol. 2019;17(1):26–40. doi: 10.11989/JEST.1674-862X.80904120.
   Google Scholar
• Nikbakht S, Anitescu C, Rabczuk T. Optimizing the neural network hyperparameters utilizing genetic algorithm. J Zhejiang Univ Sci A. 2021;22(6):407–426. doi: 10.1631/jzus.A2000384.
   Web of Science ®Google Scholar
• Ho DSW, Schierding W, Wake M, et al. Machine learning SNP based prediction for precision medicine. Front Genet. 2019;10:267. 10.3389/fgene.2019.00267.
   PubMed Web of Science ®Google Scholar
• López‐Bigas N, Ouzounis CA. Genome‐wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res. 2004;32(10):3108–3114. doi: 10.1093/nar/gkh605.
   PubMed Web of Science ®Google Scholar
• Statzer C, Luthria K, Sharma A, et al. The human extracellular matrix diseasome reveals genotype–phenotype associations with clinical implications for Age-Related diseases. Biomedicines. 2023;11(4):1212. doi: 10.3390/biomedicines11041212.
   PubMed Web of Science ®Google Scholar
• Bastarache L, Delozier SB, Pandit A, et al. The Phenotype-Genotype reference map: improving biobank data science through replication. bioRxiv. 2022. doi: 10.1101/2022.09.07.506932.
   Google Scholar
• Colona VL, Biancolella M, Novelli A, et al. Will GWAS eventually allow the identification of genomic biomarkers for COVID-19 severity and mortality? J Clin Invest. 2021;131(23):e155011. doi: 10.1172/JCI155011.
   PubMed Web of Science ®Google Scholar
• Medvedev A, Sharma SM, Tsatsorin E, et al. Human genotype-to-phenotype predictions: boosting accuracy with nonlinear models. PLoS One. 2022;17(8):e0273293. doi: 10.1371/journal.pone.0273293.
   PubMed Web of Science ®Google Scholar
• Abraham G, Kowalczyk A, Zobel J, et al. SparSNP: fast and Memory-Efficient analysis of all SNPs for phenotype prediction. BMC Bioinf. 2012;13(1):88. doi: 10.1186/1471-2105-13-88.
   PubMedGoogle Scholar
• Gorodeski EZ, Ishwaran H, Kogalur UB, et al. Use of hundreds of electrocardiographic biomarkers for prediction of mortality in postmenopausal women. Circ Cardiovasc Qual Outcomes. 2011;4(5):521–532. doi: 10.1161/CIRCOUTCOMES.110.959023.
   PubMed Web of Science ®Google Scholar
• López B, Torrent-Fontbona F, Viñas R, et al. Single nucleotide polymorphism relevance learning with random forests for type 2 diabetes risk prediction. Artif Intell Med. 2018;85:43–49. doi: 10.1016/j.artmed.2017.09.005.
   PubMed Web of Science ®Google Scholar
• Abraham G, Rohmer A, Tye-Din JA, et al. Genomic prediction of celiac disease targeting HLA-Positive individuals. Genome Med. 2015;7(1):72. doi: 10.1186/s13073-015-0196-5.
   PubMedGoogle Scholar
• Tan AC, Gilbert D. Ensemble Machine Learning on Gene Expression Data for Cancer Classification. University of Glasgow; 2003. http://bura.brunel.ac.uk/handle/2438/3013.
   Google Scholar
• Snousy MBA, El-Deeb HM, Badran K, et al. Suite of decision Tree-Based classification algorithms on cancer gene expression data. Egypt Informatics J. 2011;12(2):73–82. doi: 10.1016/j.eij.2011.04.003.
   Google Scholar
• Che D, Liu Q, Rasheed K, et al. Decision tree and ensemble learning algorithms with their applications in bioinformatics. In: arabnia HR, tran QN, editors. Software tools and algorithms for biological systems. Adv Exp Med Biol. 2011;696:191–199. doi: 10.1007/978-1-4419-7046-6_19.
   PubMed Web of Science ®Google Scholar
• Soh KP, Szczurek E, Sakoparnig T, et al. Predicting cancer type from tumour DNA signatures. Genome Med. 2017;9(1):104. doi: 10.1186/s13073-017-0493-2.
   PubMedGoogle Scholar
• Lu TP, Kuo KT, Chen CH, et al. Developing a prognostic gene panel of epithelial ovarian cancer patients by a machine learning model. Cancers (Basel). 2019;11(2):270. doi: 10.3390/cancers11020270.
   PubMed Web of Science ®Google Scholar
• Hao J, Kim Y, Mallavarapu T, et al. Cox-PASNet: pathway-Based sparse deep neural network for survival analysis. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018. pp. 381–386. doi: 10.1109/BIBM.2018.8621345.
   Google Scholar
• López-García G, Jerez JM, Franco L, et al. Transfer learning with convolutional neural networks for cancer survival prediction using Gene-Expression data. PLoS One. 2020;15(3):e0230536. doi: 10.1371/journal.pone.0230536.
   PubMed Web of Science ®Google Scholar
• Wu H, Guo C, Wang C, et al. Single‐cell RNA sequencing reveals tumor heterogeneity, microenvironment, and drug‐resistance mechanisms of recurrent glioblastoma. Cancer Sci. 2023;114(6):2609–2621. doi: 10.1111/cas.15773.
   PubMed Web of Science ®Google Scholar
• Neftel C, Laffy J, Filbin MG, et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell. 2019;178(4):835–849.e21. doi: 10.1016/j.cell.2019.06.024.
   PubMed Web of Science ®Google Scholar
• Cao Y, Zhang X, Chen Q, et al. Patient-derived organoid facilitating personalized medicine in gastrointestinal stromal tumor with liver metastasis: a case report. Front Oncol. 2022;12:920762. doi: 10.3389/fonc.2022.920762.
   PubMed Web of Science ®Google Scholar
• Muzzey D, Evans EA, Lieber C. Understanding the basics of NGS: from mechanism to variant calling. Curr Genet Med Rep. 2015;3(4):158–165. doi: 10.1007/s40142-015-0076-8.
   PubMedGoogle Scholar
• Ainscough BJ, Barnell EK, Ronning P, et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet. 2018;50(12):1735–1743. doi: 10.1038/s41588-018-0257-y.
   PubMed Web of Science ®Google Scholar
• Li S, van der Velde KJ, de Ridder D, et al. CAPICE: a computational method for Consequence-Agnostic pathogenicity interpretation of clinical exome variations. Genome Med. 2020;12(1):75. doi: 10.1186/s13073-020-00775-w.
   PubMed Web of Science ®Google Scholar
• Huang YF, Gulko B, Siepel A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat Genet. 2017;49(4):618–624. doi: 10.1038/ng.3810.
   PubMed Web of Science ®Google Scholar
• Leung MKK, Xiong HY, Lee LJ, et al. Deep learning of the Tissue-Regulated splicing code. Bioinformatics. 2014;30(12):i121–i129. doi: 10.1093/bioinformatics/btu277.
   PubMed Web of Science ®Google Scholar
• López-Bigas N, Audit B, Ouzounis C, et al. Are splicing mutations the most frequent cause of hereditary disease? FEBS Lett. 2005;579(9):1900–1903. doi: 10.1016/j.febslet.2005.02.047.
   PubMed Web of Science ®Google Scholar
• Lin YL, Chang PC, Hsu C, et al. Comparison of GATK and DeepVariant by trio sequencing. Sci Rep. 2022;12(1):1809. doi: 10.1038/s41598-022-05833-4.
   PubMed Web of Science ®Google Scholar
• Supernat A, Vidarsson OV, Steen VM, et al. Comparison of three variant callers for human whole genome sequencing. Sci Rep. 2018;8(1):17851. doi: 10.1038/s41598-018-36177-7.
   PubMedGoogle Scholar
• Schmidt B, Hildebrandt A. Deep learning in Next-Generation sequencing. Drug Discov Today. 2021;26(1):173–180. doi: 10.1016/j.drudis.2020.10.002.
   PubMed Web of Science ®Google Scholar
• Telenti A, Lippert C, Chang PC, et al. Deep learning of genomic variation and regulatory network data. Hum Mol Genet. 2018;27(R1):R63–R71. doi: 10.1093/hmg/ddy115.
   PubMed Web of Science ®Google Scholar
• Kircher M, Witten DM, Jain P, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46(3):310–315. doi: 10.1038/ng.2892.
   PubMed Web of Science ®Google Scholar
• Rentzsch P, Witten D, Cooper GM, et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886–D894. doi: 10.1093/nar/gky1016.
   PubMed Web of Science ®Google Scholar
• Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31(5):761–763. doi: 10.1093/bioinformatics/btu703.
   PubMed Web of Science ®Google Scholar
• Zhang H, Xu MS, Fan X, et al. Predicting functional effect of missense variants using graph attention neural networks. Nat Mach Intell. 2022;4(11):1017–1028. doi: 10.1038/s42256-022-00561-w.
   PubMedGoogle Scholar
• Sundaram L, Gao H, Padigepati SR, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50(8):1161–1170. doi: 10.1038/s41588-018-0167-z.
   PubMed Web of Science ®Google Scholar
• Savarese M, Välipakka S, Johari M, et al. Is Gene-Size an issue for the diagnosis of skeletal muscle disorders? J Neuromuscul Dis. 2020;7(3):203–216. doi: 10.3233/JND-190459.
   PubMedGoogle Scholar
• Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods. 2015;12(10):931–934. doi: 10.1038/nmeth.3547.
   PubMed Web of Science ®Google Scholar
• de Sainte Agathe JM, Filser M, Isidor B, et al. SpliceAI-Visual: a free online tool to improve SpliceAI splicing variant interpretation. Hum Genomics. 2023;17(1):7. 7 doi: 10.1186/s40246-023-00451-1.
   PubMed Web of Science ®Google Scholar
• Park S, Koh Y, Jeon H, et al. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism. Sci Rep. 2020;10(1):13413. doi: 10.1038/s41598-020-70218-4.
   PubMed Web of Science ®Google Scholar
• Bu F, Zhong M, Chen Q, et al. DVPred: a Disease-Specific prediction tool for variant pathogenicity classification for hearing loss. Hum Genet. 2022;141(3-4):401–411. doi: 10.1007/s00439-022-02440-1.
   PubMed Web of Science ®Google Scholar
• Do BT, Golkov V, Gürel GE, et al. Precursor MicroRNA identification using deep convolutional neural networks. bioRxiv. 2018. doi: 10.1101/414656.
   Google Scholar
• Nidheesh NK, Abdul Nazeer A, Ameer PM. An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data. Comput Biol Med. 2017;91(12):213–221. doi: 10.1016/j.compbiomed.2017.10.014.
   PubMedGoogle Scholar
• Gupta A, Wang H, Ganapathiraju M. Learning structure in gene expression data using deep architectures, with an application to gene clustering. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2015. pp. 1328–1335. doi: 10.1109/BIBM.2015.7359871.
   Google Scholar
• Arora A, Olshen AB, Seshan VE, et al. Pan-Cancer identification of clinically relevant genomic subtypes using Outcome-Weighted integrative clustering. Genome Med. 2020;12(1):110. doi: 10.1186/s13073-020-00804-8.
   PubMed Web of Science ®Google Scholar
• Young JD, Cai C, Lu X. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma. BMC Bioinf. 2017;18(S11):381. doi: 10.1186/s12859-017-1798-2.
   PubMedGoogle Scholar
• Cao J, Gong J, Li X, et al. Unsupervised hierarchical clustering identifies immune gene subtypes in gastric cancer. Front Pharmacol. 2021;12:692454. 10.3389/fphar.2021.692454.
   PubMed Web of Science ®Google Scholar
• Mallavarapu T, Hao J, Kim Y, et al. Pathway-Based deep clustering for molecular subtyping of cancer. Methods. 2020;173(2):24–31. doi: 10.1016/j.ymeth.2019.06.017.
   PubMedGoogle Scholar
• Simidjievski N, Bodnar C, Tariq I, et al. Variational autoencoders for cancer data integration: design principles and computational practice. Front Genet. 2019;10:1205. doi: 10.3389/fgene.2019.01205.
   PubMed Web of Science ®Google Scholar
• Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics. 2015;8(1):33. doi: 10.1186/s12920-015-0108-y.
   PubMedGoogle Scholar
• Peterson RE, Kuchenbaecker K, Walters RK, et al. Genome-Wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019;179(3):589–603. doi: 10.1016/j.cell.2019.08.051.
   PubMed Web of Science ®Google Scholar
• Teo YY, Small KS, Kwiatkowski DP. Methodological challenges of Genome-Wide association analysis in africa. Nat Rev Genet. 2010;11(2):149–160. doi: 10.1038/nrg2731.
   PubMed Web of Science ®Google Scholar
• He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36. doi: 10.1038/s41591-018-0307-0.
   PubMed Web of Science ®Google Scholar
• Koumakis L. Deep learning models in genomics; are We there yet? Comput Struct Biotechnol J. 2020;18(January):1466–1473. doi: 10.1016/j.csbj.2020.06.017.
   PubMedGoogle Scholar
• Berisha V, Krantsevich C, Hahn PR, et al. Digital medicine and the curse of dimensionality. NPJ Digit Med. 2021;4(1):153. doi: 10.1038/s41746-021-00521-5.
   PubMed Web of Science ®Google Scholar
• Shetta O, Niranjan M. Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality. R Soc Open Sci. 2020;7(2):190714. doi: 10.1098/rsos.190714.
   PubMed Web of Science ®Google Scholar
• König H, Frank D, Baumann M, et al. AI models and the future of genomic research and medicine: true sons of knowledge? Bioessays. 2021;43(10):e2100025. doi: 10.1002/bies.202100025.
   PubMed Web of Science ®Google Scholar
• Erhan D, Bengio Y, Courville A, et al. Visualizing Higher-Layer features of a deep network. Technical Report, Univeristy of Montreal. 2009;134(3):1.
   Google Scholar
• Devlin J, Chang MW, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 Long and Short Papers), 2019. pp. 4171–4186. https://aclanthology.org/N19-1423
   Google Scholar
• Anaissi A, Goyal M, Catchpoole DR, et al. Ensemble feature learning of genomic data using support vector machine. PLoS One. 2016;11(6):e0157330. doi: 10.1371/journal.pone.0157330.
   PubMed Web of Science ®Google Scholar
• Tsamardinos I, Borboudakis G, Katsogridakis P, et al. A greedy feature selection algorithm for big data of high dimensionality. Mach Learn. 2019;108(2):149–202. doi: 10.1007/s10994-018-5748-7.
   PubMed Web of Science ®Google Scholar
• Xu D, Zhang J, Xu H, et al. Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data. BMC Genomics. 2020;21(1):650. doi: 10.1186/s12864-020-07038-3.
   PubMed Web of Science ®Google Scholar
• Clark AG, Glanowski S, Nielsen R, et al. Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science. 2003;302(5652):1960–1963. doi: 10.1126/science.1088821.
   PubMed Web of Science ®Google Scholar
• Brawand D, Soumillon M, Necsulea A, et al. The evolution of gene expression levels in mammalian organs. Nature. 2011;478(7369):343–348. doi: 10.1038/nature10532.
   PubMed Web of Science ®Google Scholar
• Sahraeian SM, Liu R, Lau B, et al. Deep convolutional neural networks for accurate somatic mutation detection. Nat Commun. 2019;10(1):1041. doi: 10.1038/s41467-019-09027-x.
   PubMedGoogle Scholar
• Cai L, Wu Y, Gao J. DeepSV: accurate calling of genomic deletions from high-throughput sequencing data ­using deep convolutional neural network. BMC Bioinf. 2019;20(1):665. doi: 10.1186/s12859-019-3299-y.
   PubMed Web of Science ®Google Scholar
• Eberle MA, Fritzilas E, Krusche P, et al. A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree. Genome Res. 2017;27(1):157–164. doi: 10.1101/gr.210500.116.
   PubMed Web of Science ®Google Scholar
• Friedman S, Gauthier L, Farjoun Y, et al. Lean and deep models for more accurate filtering of SNP and INDEL variant calls. Bioinformatics. 2020;36(7):2060–2067. doi: 10.1093/bioinformatics/btz901.
   PubMed Web of Science ®Google Scholar
• Tran VDT, Tempel S, Zerath B, et al. miRBoost: boosting support vector machines for microRNA precursor classification. RNA. 2015;21(5):775–785. doi: 10.1261/rna.043612.113.
   PubMed Web of Science ®Google Scholar
• Boža V, Brejová B, Vinař T. DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads. PLoS One. 2017;12(6):e0178751. doi: 10.1371/journal.pone.0178751.
   PubMed Web of Science ®Google Scholar
• Agius P, Ying Y, Campbell C. Bayesian unsupervised learning with multiple data types. Stat Appl Genet Mol Biol. 2009;8(1):27–27. doi: 10.2202/1544-6115.1441.
   Web of Science ®Google Scholar
• Monti S, Tamayo P, Mesirov J, et al. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52(1/2):91–118. doi: 10.1023/A:1023949509487.
   Web of Science ®Google Scholar