Abstract

Artificial intelligence (AI) has emerged as a powerful tool in human genetics, enabling the analysis and interpretation of complex datasets generated by nextgeneration sequencing, single-cell profiling, and large population biobanks. Traditional statistical and computational methods struggle with the scale, noise, and heterogeneity of these data, whereas AI approaches, particularly machine learning (ML) and deep learning (DL), are uniquely suited to uncover hidden patterns and make clinically relevant predictions. Current applications of AI in genetics include identifying the possible effects of genomic mutations, data base genome mapping, genomic control, and association of different biological data. There has also been some progress in diagnostics of rare diseases, pharmacogenomics, genome-wide association studies (GWAS), and polygenic risk scores analysis. AI has also influenced precision medicine. The use of deep variant, alpha fold, and AI-aided clinical tools are important milestones to note in the arms of genomic medicine. Regardless of progress clinical decision support systems still face challenges like lack interpret interface, reproducibility of data, and equity issues related to privacy. This review aims to describe the dominions in the application AI to human genetics, success tracking, flaws and relief for AI stems from.

• 1.   LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444.
• 2.   Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
• 3.   Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321–332.
• 4.   Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12:878.
• 5.   Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–589.
• 6.   Jaganathan K, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176:535–548.e24.
• 7.   Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods. 2015;12:931–934.
• 8.   Argelaguet R, et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14:e8124.
• 9.   Kourou K, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.
• 10.   Mavaddat N, et al. Polygenic risk scores for prediction of breast cancer and its subtypes. Am J Hum Genet. 2019;104:21–34.
• 11.   Huang S, Chaudhary K, Garmire LX. More is better: recent progress in multi-omics data integration methods. Front Genet. 2017;8:84.
• 12.   Zitnik M, et al. Machine learning for integrating biological networks. Curr Opin Biotechnol. 2019;58:17–23.
• 13.   Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15:1053– 1058.
• 14.   Lee SI, Celik S, Logsdon BA, et al. A machine learning approach to integrate big data for precision medicine in cancer. Nat Rev Genet. 2018;19:298–310.
• 15.   Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V. Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072. 2015.
• 16.   Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–64.
• 17.   Clark MM, et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping. Sci Transl Med. 2019;11:eaat6177.
• 18.   Poplin R, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36:983–987.
• 19.   Luo R, Sedlazeck FJ, Lam TW, Schatz MC. Clairvoyante: a multi-task convolutional deep neural network for variant calling in single molecule sequencing. Nat Commun. 2019;10:998.
• 20.   Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–589.
• 21.   Tunyasuvunakool K, et al. Accurate prediction of protein structures for the human proteome. Nature. 2021;596:590–596.
• 22.   Gurovich Y, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–64.
• 23.   Loos R, et al. AI-assisted diagnosis in rare diseases. NPJ Genom Med. 2020;5:53.
• 24.   Kourou K, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.
• 25.   Mavaddat N, et al. Polygenic risk scores for prediction of breast cancer and its subtypes. Am J Hum Genet. 2019;104:21–34.
• 26.   Argelaguet R, et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14:e8124.
• 27.   Zitnik M, et al. Machine learning for integrating biological networks. Curr Opin Biotechnol. 2019;58:17–23.
• 28.   Chen X, et al. The challenges of AI in genomics: data quality and heterogeneity. Nat Rev Genet. 2020;21:445–458.
• 29.   Huang S, et al. More is better: recent progress in multi-omics data integration. Front Genet. 2017;8:84.
• 30.   Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1:206–215.
• 31.   Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765–4774.
• 32.   Martin AR, et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–591.
• 33.   Sirugo G, Williams SM, Tishkoff SA. The missing diversity in human genetic studies. Cell. 2019;177:26–31.
• 34.   Angermueller C, et al. Deep learning for computational biology. Mol Syst Biol. 2016;12:878.
• 35.   Gymrek M, et al. Identifying personal genomes by surname inference. Science. 2013;339: 321–324.
• 36.   Shabani M, Borry P. Rules for processing genetic data: ethical considerations. Genome Med. 2018;10:72.
• 37.   Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv:1702.08608. 2017.
• 38.   Chuai G, et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biol. 2018;19:80.
• 39.   Schork NJ, Goetz LH. Personalized medicine: from genome to digital twin. Nat Rev Genet. 2017;18:291–300.
• 40.   Yuan H, et al. Multi-omics data integration for precision medicine. Front Genet. 2020;11:131.
• 41.   Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016;538:161–164.
• 42.   Vayena E, et al. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2018;15:e1002689.

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Funding