Treffer: Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.

Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.

Olawade DB; Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK. d.olawade@yorksj.ac.uk.; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham, ME7 5NY, UK. d.olawade@yorksj.ac.uk.; Department of Public Health, York St John University, London, UK. d.olawade@yorksj.ac.uk., Kade A; School of Life Sciences, Gibbet Hill, University of Warwick, Warwick, UK., Egbon E; Department of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, Vienna, Austria., Usman SO; Department of Systems and Industrial Engineering, University of Arizona, Tucson, USA., Fapohunda O; Department of Chemistry and Biochemistry, University of Arizona, Tucson, USA., Ijiwade J; Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan, Nigeria., Ogbonna CE; Department of Civil/Industrial Engineering, Bahçeşehir Cyprus University, Lefkosa-Guzelyurt, Alaykoy, Mersin-10, Turkey.

Molecular genetics and genomics : MGG [Mol Genet Genomics] 2025 Dec 05; Vol. 300 (1), pp. 111. Date of Electronic Publication: 2025 Dec 05.

Journal Article; Review

English

Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101093320 Publication Model: Electronic Cited Medium: Internet ISSN: 1617-4623 (Electronic) Linking ISSN: 16174623 NLM ISO Abbreviation: Mol Genet Genomics Subsets: MEDLINE

Original Publication: Berlin : Springer-Verlag, c2001-

Genomics*/methods , Genomics*/trends , Artificial Intelligence*/trends , Computational Biology*/methods , Computational Biology*/trends, Humans ; Deep Learning ; High-Throughput Nucleotide Sequencing ; Machine Learning ; Data Analysis

Aarif KM, Hasan VMY, Alam A, Ali KS, Pakruddin B (2025) Decoding DNA: deep learning’s impact on genomic exploration. Deep learning in genetics and genomics. Academic Press, pp 77–95. https://doi.org/10.1016/B978-0-443-27574-6.00005-9. (PMID: 10.1016/B978-0-443-27574-6.00005-9)
Abdelwahab O, Torkamaneh D (2025) Artificial intelligence in variant calling: a review. Front Bioinform 5:1574359. https://doi.org/10.3389/fbinf.2025.1574359. (PMID: 10.3389/fbinf.2025.15743594033752512055765)
Abdi G, Tarighat MA, Jain M, Tendulkar R, Tendulkar M, Barwant M (2024) Revolutionizing genomics: exploring the potential of next-generation sequencing. Advances in bioinformatics. Springer Nature Singapore, Singapore, pp 1–33. https://doi.org/10.1007/978-981-99-8401-5_1. (PMID: 10.1007/978-981-99-8401-5_1)
Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Jumper JM (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630(8016):493–500. https://doi.org/10.1038/s41586-024-07487-w. (PMID: 10.1038/s41586-024-07487-w3871883511168924)
Acharya D, Mukhopadhyay A (2024) A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Brief Funct Genomics. https://doi.org/10.1093/bfgp/elae013. (PMID: 10.1093/bfgp/elae01338600757)
Agarwal N, Nupur, Paul PK, Mishra SK (2024) Artificial intelligence and machine learning for analysis of multi-omics. Multi-omics analysis of the human microbiome: from technology to clinical applications. Springer Nature Singapore, Singapore, pp 339–354. https://doi.org/10.1007/978-981-97-1844-3_16. (PMID: 10.1007/978-981-97-1844-3_16)
Ahmad S, Bashar A, Khanna K, Bano N, Raza K (2025) Deep learning in variant detection and annotation. Deep learning in genetics and genomics. Academic Press, pp 383–396. https://doi.org/10.1016/B978-0-443-27574-6.00010-2. (PMID: 10.1016/B978-0-443-27574-6.00010-2)
Ahmed Z, Mohamed K, Zeeshan S, Dong X (2020) Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020:baaa010. https://doi.org/10.1093/database/baaa010. (PMID: 10.1093/database/baaa010321853967078068)
Ahmed A, Xi R, Hou M, Shah SA, Hameed S (2023a) Harnessing big data analytics for healthcare: a comprehensive review of frameworks, implications, applications, and impacts. IEEE Access 11:112891–112928. https://doi.org/10.1109/ACCESS.2023.3323574. (PMID: 10.1109/ACCESS.2023.3323574)
Ahmed Z, Zeeshan S, Lee D (2023b) Editorial: artificial intelligence for personalized and predictive genomics data analysis. Front Genet. https://doi.org/10.3389/fgene.2023.1162869. (PMID: 10.3389/fgene.2023.11628693816268110757615)
Akhoon N (2021) Precision medicine: a new paradigm in therapeutics. Int J Prev Med 12:12. https://doi.org/10.4103/ijpvm.IJPVM_375_19. (PMID: 10.4103/ijpvm.IJPVM_375_19340843098106271)
Athaya T, Ripan RC, Li X, Hu H (2023) Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform 24:bbad313. https://doi.org/10.1093/bib/bbad313. (PMID: 10.1093/bib/bbad3133765160710516349)
Aundhia C, Parmar G, Talele C, Shah N, Talele D (2024) Impact of artificial intelligence on drug development and delivery. CTMC. https://doi.org/10.2174/0115680266324522240725053634. (PMID: 10.2174/0115680266324522240725053634)
Babu BK (2024) Personalized medicine and advancements in pharmacology: shaping the future of healthcare. Int J Pharm Investig 14:338–342. https://doi.org/10.5530/ijpi.14.2.41. (PMID: 10.5530/ijpi.14.2.41)
Ballard JL, Wang Z, Li W, Shen L, Long Q (2024) Deep learning-based approaches for multi-omics data integration and analysis. BioData Min 17(1):38. https://doi.org/10.1186/s13040-024-00391-z. (PMID: 10.1186/s13040-024-00391-z3935879311446004)
Banimfreg BH (2023) A comprehensive review and conceptual framework for cloud computing adoption in bioinformatics. Healthcare Anal 3:100190. https://doi.org/10.1016/j.health.2023.100190. (PMID: 10.1016/j.health.2023.100190)
Barbierato E, Gatti A (2024) The challenges of machine learning: a critical review. Electronics. https://doi.org/10.3390/electronics13020416. (PMID: 10.3390/electronics13020416)
Baysoy A, Bai Z, Satija R, Fan R (2023a) The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 24(10):695–713. https://doi.org/10.1038/s41580-023-00615-w. (PMID: 10.1038/s41580-023-00615-w37280296)
Baysoy A, Bai Z, Satija R, Fan R (2023b) The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 24:695–713. https://doi.org/10.1038/s41580-023-00615-w. (PMID: 10.1038/s41580-023-00615-w37280296)
Bell CG (2024) Epigenomic insights into common human disease pathology. Cell Mol Life Sci 81(1):178. https://doi.org/10.1007/s00018-024-05206-2. (PMID: 10.1007/s00018-024-05206-23860253511008083)
Benegas G, Ye C, Albors C, Li JC, Song YS (2025) Genomic language models: opportunities and challenges. Trends Genet. https://doi.org/10.1016/j.tig.2024.11.013. (PMID: 10.1016/j.tig.2024.11.01339753409)
Berisha V, Krantsevich C, Hahn PR, Hahn S, Dasarathy G, Turaga P, Liss J (2021) Digital medicine and the curse of dimensionality. NPJ Digit Med 4(1):153. https://doi.org/10.1038/s41746-021-00521-5. (PMID: 10.1038/s41746-021-00521-5347119248553745)
Bhattacharjya A, Islam MM, Uddin MA, Talukder MA, Azad A, Aryal S, Paul BK, Tasnim W, Almoyad MAA, Moni MA (2024) Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR -mutated lung adenocarcinoma. FEBS Open Bio 14:1166–1191. https://doi.org/10.1002/2211-5463.13807. (PMID: 10.1002/2211-5463.138073878363911216941)
Bhinder B, Gilvary C, Madhukar N, Elemento O (2021) Artificial intelligence in cancer research and precision medicine. Cancer Discov 11(4):900–915. https://doi.org/10.1158/2159-8290.CD-21-0090. (PMID: 10.1158/2159-8290.CD-21-0090338111238034385)
Black JR, McGranahan N (2021) Genetic and non-genetic clonal diversity in cancer evolution. Nat Rev Cancer 21(6):379–392. https://doi.org/10.1038/s41568-021-00336-2. (PMID: 10.1038/s41568-021-00336-233727690)
Boutros A, Arora A, Betz V. (2024). Field-programmable gate array architecture for deep learning: survey & future directions. arXiv preprint arXiv:2404.10076 . https://doi.org/10.48550/arXiv.2404.10076.
Brlek P, Bulić L, Bračić M, Projić P, Škaro V, Shah N, Shah P, Primorac D (2024) Implementing whole genome sequencing (WGS) in clinical practice: advantages, challenges, and future perspectives. Cells 13:504. https://doi.org/10.3390/cells13060504. (PMID: 10.3390/cells130605043853434810969765)
Buermans HP, den Dunnen JT (2014) Next generation sequencing technology: advances and applications. Biochim Biophys Acta (BBA)-Mol Basis Dis 1842(10):1932–1941. https://doi.org/10.1016/j.bbadis.2014.06.015. (PMID: 10.1016/j.bbadis.2014.06.015)
Calvino G, Peconi C, Strafella C, Trastulli G, Megalizzi D, Andreucci S, Cascella R, Caltagirone C, Zampatti S, Giardina E (2024) Federated learning: breaking down barriers in global genomic research. Genes 15(22):1650. https://doi.org/10.3390/genes15121650. (PMID: 10.3390/genes151216503976691711728131)
Carini C, Seyhan AA (2024) Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med 22:411. https://doi.org/10.1186/s12967-024-05067-0. (PMID: 10.1186/s12967-024-05067-03870271111069149)
Catacutan DB, Alexander J, Arnold A, Stokes JM (2024) Machine learning in preclinical drug discovery. Nat Chem Biol 20:960–973. https://doi.org/10.1038/s41589-024-01679-1. (PMID: 10.1038/s41589-024-01679-139030362)
Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T (2021) AI applications in functional genomics. Comput Struct Biotechnol J 19:5762–5790. https://doi.org/10.1016/j.csbj.2021.10.009. (PMID: 10.1016/j.csbj.2021.10.009347650938566780)
Chafai N, Bonizzi L, Botti S, Badaoui B (2024) Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 61:140–163. https://doi.org/10.1080/10408363.2023.2259466. (PMID: 10.1080/10408363.2023.225946637815417)
Chakraborty S, Sharma G, Karmakar S, Banerjee S (2024) Multi-OMICS approaches in cancer biology: new era in cancer therapy. Biochim Biophys Acta (BBA) - Mol Basis Dis 1870:167120. https://doi.org/10.1016/j.bbadis.2024.167120. (PMID: 10.1016/j.bbadis.2024.167120)
Chandra A, Goyal N, Behera A, Samanta J, Sharma T (2024) Chapter 16 - Artificial intelligence-based protein structure prediction and systems biology-guided smart drug screening. In: Rudrapal M, Egbuna C, Cho WC (eds) Biochemical and molecular pharmacology in drug discovery, drug discovery update. Elsevier, pp 323–352. https://doi.org/10.1016/B978-0-443-16013-4.00016-6. (PMID: 10.1016/B978-0-443-16013-4.00016-6)
Chandrashekar K, Niranjan V, Vishal A, Setlur AS (2024) Integration of artificial intelligence, machine learning and deep learning techniques in genomics: review on computational perspectives for NGS analysis of DNA and RNA seq data. Curr Bioinform 19(9):825–844. https://doi.org/10.2174/0115748936284044240108074937. (PMID: 10.2174/0115748936284044240108074937)
Chehelgerdi M, Chehelgerdi M, Khorramian-Ghahfarokhi M, Shafieizadeh M, Mahmoudi E, Eskandari F, Rashidi M, Arshi A, Mokhtari-Farsani A (2024) Comprehensive review of CRISPR-based gene editing: mechanisms, challenges, and applications in cancer therapy. Mol Cancer 23:9. https://doi.org/10.1186/s12943-023-01925-5. (PMID: 10.1186/s12943-023-01925-53819553710775503)
Chen S, & Guo W (2023) Auto-encoders in deep learning—a review with new perspectives. Mathematics 11(8), 1777. https://doi.org/10.3390/math11081777.
Chen N, Kolesnikov A, Goel S, Yun T, Chang P, Carroll A (2023a) Improving variant calling using population data and deep learning. BMC Bioinformatics. https://doi.org/10.1186/s12859-023-05294-0. (PMID: 10.1186/s12859-023-05294-03810405710724984)
Chen W, Liu X, Zhang S, Chen S (2023b) Artificial intelligence for drug discovery: resources, methods, and applications. Mol Ther Nucleic Acids 31:691–702. https://doi.org/10.1016/j.omtn.2023.02.019. (PMID: 10.1016/j.omtn.2023.02.0193692395010009646)
Chen L, Li Q, Nasif K, Xie Y, Deng B, Niu S, Pouriyeh S, Dai Z, Chen J, Xie C (2024) Ai-driven deep learning techniques in protein structure prediction. Int J Mol Sci. https://doi.org/10.3390/ijms25158426. (PMID: 10.3390/ijms251584263979628511720278)
Chen YM, Hsiao TH, Lin CH, Fann YC (2025) Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence. J Biomed Sci 32(1):16. https://doi.org/10.1186/s12929-024-01110-w. (PMID: 10.1186/s12929-024-01110-w3991578011804102)
Chicco D, Cumbo F, Angione C (2023) Ten quick tips for avoiding pitfalls in multi-omics data integration analyses. PLoS Comput Biol 19:e1011224. https://doi.org/10.1371/journal.pcbi.1011224. (PMID: 10.1371/journal.pcbi.10112243741070410325053)
Chinta SV, Wang Z, Zhang X, Viet TD, Kashif A, Smith MA, Zhang W 2024. AI-driven healthcare: a survey on ensuring fairness and mitigating bias. https://doi.org/10.48550/ARXIV.2407.19655 .
Choudhury C, Murugan A, Priyakumar U (2022) Structure-based drug repurposing: traditional and advanced AI/ML-aided methods. Drug Discov Today 27:1847–1861. https://doi.org/10.1016/j.drudis.2022.03.006. (PMID: 10.1016/j.drudis.2022.03.006353011488920090)
Chugh V, Basu A, Kaushik A, Bhansali S, Basu AK (2024) Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer. Nanoscale 16:5458–5486. https://doi.org/10.1039/D3NR05648A. (PMID: 10.1039/D3NR05648A38391246)
Clark AJ, Lillard Jr (2024) A comprehensive review of bioinformatics tools for genomic biomarker discovery driving precision oncology. Genes 15(8):1036. https://doi.org/10.3390/genes15081036. (PMID: 10.3390/genes150810363920239711353282)
CNCB-NGDC Members and Partners, Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, Cen H, Chao J, Chen F, Chen H, Chen K, Chen M, Chen M, Chen M, Chen Q, Chen R, Chen S, Chen T, Chen X, Chen Xu, Cheng Y, Chu Y, Cui Q, Dong L, Du Z, Duan G, Fan S, Fan Z, Fang X, Fang Z, Feng Z, Fu S, Gao F, Gao G, Gao H, Gao W, Gao X, Gao X, Gao X, Gong J, Gong J, Gou Y, Gu S, Guo A-Y, Guo G, Guo X, Han C, Hao D, Hao L, He Q, He S, He S, Hu W, Huang K, Huang T, Huang X, Huang Y, Jia P, Jia Y, Jiang C, Jiang M, Jiang S, Jiang T, Jiang X, Jin E, Jin W, Kang H, Kang H, Kong D, Lan L, Lei W, Li C-Y, Li C, Li C, Li H, Li J, Li J, Li L, Li P, Li R, Li X, Li Y, Li Y, Li Z, Liao X, Lin S, Lin Y, Ling Y, Liu B, Liu C-J, Liu D, Liu G-H, Liu L, Liu S, Liu W, Liu X, Liu X, Liu Y, Liu Y, Lu M, Lu T, Luo H, Luo H, Luo M, Luo S, Luo X, Ma L, Ma Y, Mai J, Meng J, Meng X, Meng Y, Meng Y, Miao W, Miao Y-R, Ni L, Nie Z, Niu G, Niu X, Niu Y, Pan R, Pan S, Peng D, Peng J, Qi J, Qi Y, Qian Q, Qin Y, Qu H, Ren J, Ren J, Sang Z, Shang K, Shen W-K, Shen Y, Shi Y, Song S, Song T, Su T, Sun J, Sun Y, Sun Y, Sun Y, Tang B, Tang D, Tang Q, Tang Z, Tian D, Tian F, Tian W, Tian Z, Wang A, Wang G, Wang G, Wang J, Wang J, Wang P, Wang P, Wang W, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wei H, Wei Y, Wei Z, Wu D, Wu G, Wu S, Wu S, Wu W, Wu W, Wu Z, Xia Z, Xiao J, Xiao L, Xiao Y, Xie G, Xie G-Y, Xie J, Xie Y, Xiong J, Xiong Z, Xu D, Xu S, Xu T, Xu T, Xue Y, Xue Yu, Yan C, Yang D, Yang F, Yang F, Yang H, Yang J, Yang K, Yang N, Yang Q-Y, Yang S, Yang X, Yang X, Yang X, Yang Y-G, Ye W, Yu C, Yu F, Yu S, Yuan C, Yuan H, Zeng J, Zhai S, Zhang C, Zhang F, Zhang G, Zhang M, Zhang P, Zhang Q, Zhang R, Zhang S, Zhang W, Zhang W, Zhang W, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang YE, Zhang Y, Zhang Z, Zhang Z, Zhao D, Zhao F, Zhao G, Zhao M, Zhao W, Zhao W, Zhao X, Zhao Y, Zhao Y, Zhao Z, Zheng X, Zheng Y, Zhou C, Zhou H, Zhou X, Zhou X, Zhou Y, Zhou Y, Zhu J, Zhu L, Zhu R, Zhu T, Zong W, Zou D, Zuo Z (2024) Database resources of the national genomics data center, china national center for bioinformation in 2024. Nucleic Acids Res 52:D18–D32. https://doi.org/10.1093/nar/gkad1078. (PMID: 10.1093/nar/gkad1078)
Collí-Dulá RC, Papatheodorou I (2024) Single-cell RNA sequencing offers opportunities to explore the depth of physiology, adaptation, and biochemistry in non-model organisms exposed to pollution. Comp Biochem Physiol d: Genomics Proteomics. https://doi.org/10.1016/j.cbd.2024.101339. (PMID: 10.1016/j.cbd.2024.10133939393164)
Coorssen JR, Padula MP (2024) Proteomics—the state of the field: the definition and analysis of proteomes should be based in reality, not convenience. Proteomes 12:14. https://doi.org/10.3390/proteomes12020014. (PMID: 10.3390/proteomes120200143865137311036260)
Csala A, Zwinderman A (2019) Multivariate statistical methods for high-dimensional multiset omics data analysis. Comput Biol. https://doi.org/10.15586/computationalbiology.2019.ch5. (PMID: 10.15586/computationalbiology.2019.ch5)
Cudby J, Strelchuk S. (2024). Quantum algorithms for genome sequencing and analysis. In: 2024 IEEE international conference on quantum computing and engineering (QCE), 2: 458–459. https://doi.org/10.1109/QCE60285.2024.10354 .
Curion F, Theis FJ (2024) Machine learning integrative approaches to advance computational immunology. Genome Med 16:80. https://doi.org/10.1186/s13073-024-01350-3. (PMID: 10.1186/s13073-024-01350-33886297911165829)
da Silva RGL (2024) The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies. Glob Health 20:44. https://doi.org/10.1186/s12992-024-01049-5. (PMID: 10.1186/s12992-024-01049-5)
Dai M, Li X, Zhang Q, Liang T, Huang X, Fu Q (2024) Health research in the era of artificial intelligence: advances in gene-editing study. Medicine plus 1:100027. https://doi.org/10.1016/j.medp.2024.100027. (PMID: 10.1016/j.medp.2024.100027)
Dar MA, Wojda U (2024) Multiomics approaches in human diseases. Biological insights of multi-omics technologies in human diseases. Elsevier, pp 1–19. https://doi.org/10.1016/B978-0-443-23971-7.00001-8. (PMID: 10.1016/B978-0-443-23971-7.00001-8)
Das A (2024) Network medicine and artificial intelligence in cancer precision therapy: path to prevent drug-induced toxic side effect. Curr Opin Toxicol. https://doi.org/10.1016/j.cotox.2024.100476. (PMID: 10.1016/j.cotox.2024.100476)
Das S, Mazumder S, Alam N, Vernekar M, Dam A, Bhowmick A, Hajra S, Das J, Basu B (2024) Precision oncology in the era of genomics and artificial intelligence. J Curr Oncol Trends. https://doi.org/10.4103/jcot.jcot_3_23. (PMID: 10.4103/jcot.jcot_3_23)
Davis-Turak J, Courtney SM, Hazard ES, Glen WB Jr, da Silveira WA, Wesselman T, Harbin LP, Wolf BJ, Chung D, Hardiman G (2017) Genomics pipelines and data integration: challenges and opportunities in the research setting. Expert Rev Mol Diagn 17(3):225–237. https://doi.org/10.1080/14737159.2017.1282822. (PMID: 10.1080/14737159.2017.1282822280924715580401)
De Plano LM, Saitta A, Oddo S, Caccamo A (2024) Epigenetic changes in Alzheimer’s disease: DNA methylation and histone modification. Cells 13(8):719. https://doi.org/10.3390/cells13080719. (PMID: 10.3390/cells130807193866733311049073)
Deneault E (2024) Recent therapeutic gene editing applications to genetic disorders. CIMB 46:4147–4185. https://doi.org/10.3390/cimb46050255. (PMID: 10.3390/cimb460502553878552311119904)
Dixit S, Kumar A, Srinivasan K, Durai P, Vincent R, Krishnan N, Gomes C, Jamshidi E, Marchisio M, Ramu N (2024) Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions. Front Bioeng Biotechnol. https://doi.org/10.3389/fbioe.2023.1335901. (PMID: 10.3389/fbioe.2023.13359013826072610800897)
Duarte V, Gómez A, Corchado JM (2024) Analysis pipelines and a platform solution for next-generation sequencing data. Big data analysis and artificial intelligence for medical sciences. John Wiley & Sons Ltd, pp 219–251. https://doi.org/10.1002/9781119846567.ch11. (PMID: 10.1002/9781119846567.ch11)
Elhaddad M, Hamam S (2024) AI-driven clinical decision support systems: an ongoing pursuit of potential. Cureus 16:e57728. https://doi.org/10.7759/cureus.57728. (PMID: 10.7759/cureus.577283871172411073764)
El-Metwally S, Hamza T, Zakaria M, Helmy M (2013) Next-generation sequence assembly: four stages of data processing and computational challenges. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1003345. (PMID: 10.1371/journal.pcbi.1003345243482243861042)
Elouataoui W 2024. AI-driven frameworks for enhancing data quality in big data ecosystems: Error_Detection, correction, and metadata integration. https://doi.org/10.48550/ARXIV.2405.03870.
Ezeji IN, Adigun M, Oki O 2024. Computational complexity in explainable decision support system: a review. IFS 1–16. https://doi.org/10.3233/JIFS-219407 .
Faiyazuddin M, Rahman SJQ, Anand G, Siddiqui RK, Mehta R, Khatib MN, Sah R (2025) The impact of artificial intelligence on healthcare: a comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health Sci Rep 8(1):e70312. https://doi.org/10.1002/hsr2.70312. (PMID: 10.1002/hsr2.703123976358011702416)
Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H (2024) Artificial intelligence in plant breeding. Trends Genet. https://doi.org/10.1016/j.tig.2024.07.001. (PMID: 10.1016/j.tig.2024.07.00139117482)
Fauseweh B (2024) Quantum many-body simulations on digital quantum computers: state-of-the-art and future challenges. Nat Commun 15:2123. https://doi.org/10.1038/s41467-024-46402-9. (PMID: 10.1038/s41467-024-46402-93845904010923891)
Feldner-Busztin D, Firbas Nisantzis P, Edmunds SJ, Boza G, Racimo F, Gopalakrishnan S, Limborg MT, Lahti L, de Polavieja GG (2023) Dealing with dimensionality: the application of machine learning to multi-omics data. Bioinformatics 39:btad021. https://doi.org/10.1093/bioinformatics/btad021. (PMID: 10.1093/bioinformatics/btad021366372119907220)
Flores JE, Claborne DM, Weller ZD, Webb-Robertson B-JM, Waters KM, Bramer LM (2023) Missing data in multi-omics integration: recent advances through artificial intelligence. Front Artif Intell 6:1098308. https://doi.org/10.3389/frai.2023.1098308. (PMID: 10.3389/frai.2023.1098308368444259949722)
Frasca M, La Torre D, Repetto M, De Nicolò V, Pravettoni G, Cutica I (2024) Artificial intelligence applications to genomic data in cancer research: a review of recent trends and emerging areas. Discov Anal 2(1):10. https://doi.org/10.1007/s44257-024-00017-y. (PMID: 10.1007/s44257-024-00017-y)
Füllgrabe J, Gosal WS, Creed P, Liu S, Lumby CK, Morley DJ, Ost TWB, Vilella AJ, Yu S, Bignell H, Burns P, Charlesworth T, Fu B, Fordham H, Harding NJ, Gandelman O, Golder P, Hodson C, Li M, Lila M, Liu Y, Mason J, Mellad J, Monahan JM, Nentwich O, Palmer A, Steward M, Taipale M, Vandomme A, San-Bento RS, Singhal A, Vivian J, Wójtowicz N, Williams N, Walker NJ, Wong NCH, Yalloway GN, Holbrook JD, Balasubramanian S (2023) Simultaneous sequencing of genetic and epigenetic bases in DNA. Nat Biotechnol 41:1457–1464. https://doi.org/10.1038/s41587-022-01652-0. (PMID: 10.1038/s41587-022-01652-03674709610567558)
Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS (2024) Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 15:1331062. https://doi.org/10.3389/fphar.2024.1331062. (PMID: 10.3389/fphar.2024.13310623838429810879372)
Gawel D, Serra-Musach J, Lilja S, Aagesen J, Arenas A, Asking B, Bengnér M, Björkander J, Biggs S, Ernerudh J, Hjortswang H, Karlsson J, Köpsén M, Lee E, Lentini A, Li X, Magnusson M, Martínez-Enguita D, Matussek A, Nestor C, Schäfer S, Seifert O, Sonmez C, Stjernman H, Tjärnberg A, Wu S, Åkesson K, Shalek A, Stenmarker M, Zhang H, Gustafsson M, Benson M (2019) A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med. https://doi.org/10.1186/s13073-019-0657-3. (PMID: 10.1186/s13073-019-0657-3318923636938608)
Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M (2024) Advances in artificial intelligence for drug delivery and development: a comprehensive review. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2024.108702. (PMID: 10.1016/j.compbiomed.2024.10870238878397)
Ghosh N, Santoni D, Saha I, Felici G (2024a) Predicting transcription factor binding sites with deep learning. Int J Mol Sci. https://doi.org/10.3390/ijms25094990. (PMID: 10.3390/ijms250949903951928211545925)
Ghosh S, Zhao X, Alim M, Brudno M, Bhat M (2024b) Artificial intelligence applied to ‘omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment. Gut. https://doi.org/10.1136/gutjnl-2023-331740. (PMID: 10.1136/gutjnl-2023-33174039107087)
Girija DK, Yogeesh N, Rashmi M (2024) Chapter 9 - Deep learning for vehement gene expression exploration. In: Raza K, Barh D, Singh D, Ahmad N (eds) Deep learning applications in translational bioinformatics, advances in ubiquitous sensing applications for healthcare. Academic Press, pp 121–139. https://doi.org/10.1016/B978-0-443-22299-3.00009-8. (PMID: 10.1016/B978-0-443-22299-3.00009-8)
Goshisht MK (2024) Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges. ACS omega, 9(9):9921-9945.
Goswami S, Sharma S (2024) Artificial intelligence, quantum computing and cloud computing enabled personalized medicine in next generation sequencing bioinformatics. 2024 IEEE International conference on interdisciplinary approaches in technology and management for social innovation (IATMSI) (Vol 2). IEEE, pp 1–5. https://doi.org/10.1109/IATMSI60426.2024.10502533. (PMID: 10.1109/IATMSI60426.2024.10502533)
Gudkov M (2024). Identification and characterisation of genetic variation in human health and disease. UNSW Sydney. https://doi.org/10.26190/UNSWORKS/30367.
Gupta N, Verma VK (2019) Next-generation sequencing and its application: empowering in public health beyond reality. In Microbial technology for the welfare of society (pp. 313-341). Singapore: Springer Singapore.
Guo Y, Ye F, Sheng Q, Clark T, Samuels DC (2014) Three-stage quality control strategies for DNA re-sequencing data. Brief Bioinform 15(6):879–889. https://doi.org/10.1093/bib/bbt069. (PMID: 10.1093/bib/bbt06924067931)
Guo K, Wu M, Soo Z, Yang Y, Zhang Y, Zhang Q, Lin H, Grosser M, Venter D, Zhang G, Lu J (2023) Artificial intelligence-driven biomedical genomics. Knowl Based Syst 279:110937. https://doi.org/10.1016/j.knosys.2023.110937. (PMID: 10.1016/j.knosys.2023.110937)
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P (2021) Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 25:1315–1360. https://doi.org/10.1007/s11030-021-10217-3. (PMID: 10.1007/s11030-021-10217-3338441368040371)
Gurdasani D, Barroso I, Zeggini E, Sandhu MS (2019) Genomics of disease risk in globally diverse populations. Nat Rev Genet 20(9):520–535. https://doi.org/10.1038/s41576-019-0144-0. (PMID: 10.1038/s41576-019-0144-031235872)
Google Health (2022). The value of genomic analysis - google health. Available online at: https://health.google/health-research/genomics/ (Accessed April 28, 2024).
Hamamoto R, Komatsu M, Takasawa K, Asada K, Kaneko S (2019) Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine. Biomolecules 10(1):62. https://doi.org/10.3390/biom10010062. (PMID: 10.3390/biom10010062319059697023005)
Hanna MG, Pantanowitz L, Dash R, Harrison JH, Deebajah M, Pantanowitz J, Rashidi HH (2025) Future of artificial intelligence (AI)-machine learning (ML) trends in pathology and medicine. Mod Pathol. https://doi.org/10.1016/j.modpat.2025.100705. (PMID: 10.1016/j.modpat.2025.10070539761872)
Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, Huang K, Scardapane S, Spinelli I, Mahmud M, Hussain A (2024) Interpreting black-box models: a review on explainable artificial intelligence. Cogn Comput 16(1):45–74. https://doi.org/10.1007/s12559-023-10179-8. (PMID: 10.1007/s12559-023-10179-8)
He X, Liu X, Zuo F, Shi H, Jing J (2023) Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin Cancer Biol 88:187–200. https://doi.org/10.1016/j.semcancer.2022.12.009. (PMID: 10.1016/j.semcancer.2022.12.00936596352)
Hemstrom W, Grummer JA, Luikart G, Christie MR (2024) Next-generation data filtering in the genomics era. Nature Reviews Genetics, 25(11), 750-767.
Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung R-H, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O’Connor T, Zhao W, Kim W, Guo X, Ida Chen Y-D, The Trans-Omics in Precision Medicine Consortium, Sofer T (2024) Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep 14:12436. https://doi.org/10.1038/s41598-024-62945-9. (PMID: 10.1038/s41598-024-62945-93881642211139858)
Hu T, Chitnis N, Monos D, Dinh A (2021) Next-generation sequencing technologies: an overview. Hum Immunol 82(11):801–811. https://doi.org/10.1016/j.humimm.2021.02.012. (PMID: 10.1016/j.humimm.2021.02.01233745759)
Jamialahmadi H, Khalili-Tanha G, Nazari E, Rezaei-Tavirani M (2024) Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches. Gastroenterol Hepatol Bed Bench 17(3):241. https://doi.org/10.22037/ghfbb.v17i3.2977. (PMID: 10.22037/ghfbb.v17i3.29773930853911413381)
Jan R, Hussain A, Assad A, Khurshid S, Macha MA (2025) Challenges with multi-omics data integration. Multi-omics technology in human health and diseases. Academic Press, pp 223–242. https://doi.org/10.1016/B978-0-443-13595-8.00010-6. (PMID: 10.1016/B978-0-443-13595-8.00010-6)
Jiang W, Ye W, Tan X, Bao YJ (2025) Network-based multi-omics integrative analysis methods in drug discovery: a systematic review. BioData Min 18(1):27. https://doi.org/10.1186/s13040-025-00442-z. (PMID: 10.1186/s13040-025-00442-z4015597911954193)
Jin J, Kim J 2024. Multi-omics and artificial intelligence technology for elucidating disease pathophysiology. https://doi.org/10.20944/preprints202408.0350.v1 .
Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Jacobsson B (2023) Precision medicine in complex diseases—molecular subgrouping for improved prediction and treatment stratification. J Intern Med 294(4):378–396. https://doi.org/10.1111/joim.13640. (PMID: 10.1111/joim.136403709365410523928)
Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL (2021) Precision medicine, AI, and the future of personalized health care. Clin Trans Sci 14(1):86–93. https://doi.org/10.1111/cts.12884. (PMID: 10.1111/cts.12884)
Kalra S, Wen J, Cresswell JC, Volkovs M, Tizhoosh HR (2023) Decentralized federated learning through proxy model sharing. Nat Commun 14(1):2899. https://doi.org/10.1038/s41467-023-38569-4. (PMID: 10.1038/s41467-023-38569-43721747610203322)
Kang M, Ko E, Mersha TB (2021) A roadmap for multi-omics data integration using deep learning. Brief Bioinform 23:bbab454. https://doi.org/10.1093/bib/bbab454. (PMID: 10.1093/bib/bbab4548769688)
Karim M, Islam T, Beyan O, Lange C, Cochez M, Rebholz-Schuhmann D, Decker S (2022) Explainable AI for bioinformatics: methods, tools, and applications. Brief Bioinform. https://doi.org/10.48550/arXiv.2212.13261. (PMID: 10.48550/arXiv.2212.13261)
Katoh M, Loriot Y, Brandi G, Tavolari S, Wainberg ZA, Katoh M (2024) FGFR-targeted therapeutics: clinical activity, mechanisms of resistance and new directions. Nat Rev Clin Oncol 21:312–329. https://doi.org/10.1038/s41571-024-00869-z. (PMID: 10.1038/s41571-024-00869-z38424198)
Keagy J, Drummond CP, Gilbert KJ, Grozinger CM, Hamilton J, Hines HM, Lasky J, Logan CA, Sawers R, Wagner T (2023) Landscape transcriptomics as a tool for addressing global change effects across diverse species. Mol Ecol Resour 1755–0998:13796. https://doi.org/10.1111/1755-0998.13796. (PMID: 10.1111/1755-0998.13796)
Khalighi S, Reddy K, Midya A, Pandav K, Madabhushi A, Abedalthagafi M (2024) Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol 8:80. https://doi.org/10.1038/s41698-024-00575-0. (PMID: 10.1038/s41698-024-00575-03855363310980741)
Khoshandam M, Soltaninejad H, Hosseinkhani S, Saltanatpour Z, Goudarzi MTH, Hamidieh AA (2024a) CRISPR/Cas and artificial intelligence to improve precision medicine: future perspectives and potential limitations. Hum Gene. https://doi.org/10.1016/j.humgen.2024.201356. (PMID: 10.1016/j.humgen.2024.201356)
Khoshandam M, Soltaninejad H, Mousazadeh M, Hamidieh AA, Hosseinkhani S (2024b) Clinical applications of the CRISPR/Cas9 genome-editing system: delivery options and challenges in precision medicine. Genes Dis 11:268–282. https://doi.org/10.1016/j.gendis.2023.02.027. (PMID: 10.1016/j.gendis.2023.02.02737588217)
Khoury MJ, Holt KE (2021) The impact of genomics on precision public health: beyond the pandemic. Genome Med 13(1):67. https://doi.org/10.1186/s13073-021-00886-y. (PMID: 10.1186/s13073-021-00886-y338927938063188)
Koboldt DC (2020) Best practices for variant calling in clinical sequencing. Genome Med 12(1):91. https://doi.org/10.1186/s13073-020-00791-w. (PMID: 10.1186/s13073-020-00791-w331061757586657)
Lan K, Wang DT, Fong S, Liu LS, Wong KK, Dey N (2018) A survey of data mining and deep learning in bioinformatics. J Med Syst 42:1–20. https://doi.org/10.1007/s10916-018-1003-9. (PMID: 10.1007/s10916-018-1003-9)
Lawrence E, El-Shazly A, Seal S, Joshi CK, Liò P, Singh S, Bender A, Sormanni P, Greenig, M 2024. Understanding biology in the age of artificial intelligence. https://doi.org/10.48550/ARXIV.2403.04106 .
Lee B, Zhang S, Poleksic A, Xie L (2020) Heterogeneous multi-layered network model for omics data integration and analysis. Front Genet 10:1381. https://doi.org/10.3389/fgene.2019.01381. (PMID: 10.3389/fgene.2019.01381320639196997577)
Li B, Nabavi S (2024) A multimodal graph neural network framework for cancer molecular subtype classification. BMC bioinformatics, 25(1), 27.
Li Y, Chen L (2014) Big biological data: challenges and opportunities. Genomics Proteomics Bioinform 12(5):187–189. https://doi.org/10.1016/j.gpb.2014.10.001. (PMID: 10.1016/j.gpb.2014.10.001)
Li Q, Hu Z, Wang Y, Li L, Fan Y, King I, Song L, Li Y (2024a) Progress and opportunities of foundation models in bioinformatics. Brief Bioinform. https://doi.org/10.1093/bib/bbae548. (PMID: 10.1093/bib/bbae5484003754411685104)
Li Y-H, Li Y-L, Wei M-Y, Li G-Y (2024b) Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep 14:18994. https://doi.org/10.1038/s41598-024-70073-7. (PMID: 10.1038/s41598-024-70073-73915219411329630)
Liang W, Tadesse GA, Ho D, Fei-Fei L, Zaharia M, Zhang C, Zou J (2022) Advances, challenges and opportunities in creating data for trustworthy AI. Nat Mach Intell 4:669–677. https://doi.org/10.1038/s42256-022-00516-1. (PMID: 10.1038/s42256-022-00516-1)
Liu Y, Shen X, Gong Y, Liu Y, Song B, Zeng X (2023) Sequence alignment/map format: a comprehensive review of approaches and applications. Brief Bioinform 24:bbad320. https://doi.org/10.1093/bib/bbad320. (PMID: 10.1093/bib/bbad32037668049)
Liu X, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L (2024) Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. Brief Bioinform 25:bbae364. https://doi.org/10.1093/bib/bbae364. (PMID: 10.1093/bib/bbae3643908265211289682)
Logsdon GA, Vollger MR, Eichler EE (2020) Long-read human genome sequencing and its applications. Nature Reviews Genetics, 21(10), 597-614.
Longo L, Brcic M, Cabitza F, Choi J, Confalonieri R, Del Ser J, Guidotti R, Hayashi Y, Herrera F, Holzinger A, Jiang R (2024) Explainable artificial intelligence (XAI) 2.0: a manifesto of open challenges and interdisciplinary research directions. Inf Fusion 106:102301. https://doi.org/10.1016/j.inffus.2024.102301. (PMID: 10.1016/j.inffus.2024.102301)
López-Cortés A, Cabrera-Andrade A, Echeverría-Garcés G, Echeverría-Espinoza P, Pineda-Albán M, Elsitdie N, Bueno-Miño J, Cruz-Segundo CM, Dorado J, Pazos A, Gonzáles-Díaz H, Pérez-Castillo Y, Tejera E, Munteanu CR (2024) Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses. Sci Rep 14:19359. https://doi.org/10.1038/s41598-024-68565-7. (PMID: 10.1038/s41598-024-68565-73916904411339426)
Lowe WL, Reddy TE (2015) Genomic approaches for understanding the genetics of complex disease. Genome Res 25:1432–1441. https://doi.org/10.1101/gr.190603.115. (PMID: 10.1101/gr.190603.115264301534579328)
Madden EB, Hindorff LA, Bonham VL, Akintobi TH, Burchard EG, Baker KE, Begay RL, Carpten JD, Cox NJ, Di Francesco V, Dillard DA (2024) Advancing genomics to improve health equity. Nat Genet 56(5):752–757. https://doi.org/10.1038/s41588-024-01711-z. (PMID: 10.1038/s41588-024-01711-z3868489811096049)
Mahood EH, Kruse LH, Moghe GD (2020) Machine learning: a powerful tool for gene function prediction in plants. Appl Plant Sci 8(7):e11376. https://doi.org/10.1002/aps3.11376. (PMID: 10.1002/aps3.11376327659757394712)
Maljkovic Berry I, Melendrez MC, Bishop-Lilly KA, Rutvisuttinunt W, Pollett S, Talundzic E, Morton L, Jarman RG (2020) Next generation sequencing and bioinformatics methodologies for infectious disease research and public health: approaches, applications, and considerations for development of laboratory capacity. J Infect Dis 221(Supplement_3):S292–S307. https://doi.org/10.1093/infdis/jiz286. (PMID: 10.1093/infdis/jiz28631612214)
Manzoni C, Kia DA, Vandrovcova J, Hardy J, Wood NW, Lewis PA, Ferrari R (2018) Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 19(2):286–302. https://doi.org/10.1093/bib/bbw114. (PMID: 10.1093/bib/bbw11427881428)
Marques L, Costa B, Pereira M, Silva A, Santos J, Saldanha L, Silva I, Magalhães P, Schmidt S, Vale N (2024) Advancing precision medicine: a review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics 16:332. https://doi.org/10.3390/pharmaceutics16030332. (PMID: 10.3390/pharmaceutics160303323854322610975777)
Martin SH, Van Belleghem SM (2017) Exploring evolutionary relationships across the genome using topology weighting. Genetics 206(1):429–438. https://doi.org/10.1534/genetics.116.194720. (PMID: 10.1534/genetics.116.194720283416525419486)
Martinelli DD (2024) Machine learning for siRNA efficiency prediction: a systematic review. Health Sci Rev 11:100157. https://doi.org/10.1016/j.hsr.2024.100157. (PMID: 10.1016/j.hsr.2024.100157)
McCrea R, King R, Graham L, Börger L (2023) Realising the promise of large data and complex models. Methods Ecol Evol 14(1):4–11. https://doi.org/10.1111/2041-210X.14050. (PMID: 10.1111/2041-210X.14050)
Merelli I, Pérez-Sánchez H, Gesing S, D’Agostino D (2014) Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives. BioMed Res Int 2014(1):134023. https://doi.org/10.1155/2014/134023. (PMID: 10.1155/2014/134023252542024165507)
Misra BB, Langefeld C, Olivier M, Cox LA (2019) Integrated omics: tools, advances and future approaches. J Mol Endocrinol 62(1):R21–R45. https://doi.org/10.1530/JME-18-0055. (PMID: 10.1530/JME-18-005530006342)
Mittal S, Jena M, Pathak B (2024) Machine learning empowered next generation DNA sequencing: perspective and prospectus. Chem Sci 15:12169–12188. https://doi.org/10.1039/d4sc01714e. (PMID: 10.1039/d4sc01714e3911863011304540)
Mohammed MA, Abdulkareem KH, Dinar AM, Zapirain BG (2023) Rise of deep learning clinical applications and challenges in omics data: a systematic review. Diagnostics 13:664. https://doi.org/10.3390/diagnostics13040664. (PMID: 10.3390/diagnostics13040664368321529955380)
Mohr AE, Ortega-Santos CP, Whisner CM, Klein-Seetharaman J, Jasbi P (2024) Navigating challenges and opportunities in multi-omics integration for personalized healthcare. Biomedicines 12:1496. https://doi.org/10.3390/biomedicines12071496. (PMID: 10.3390/biomedicines120714963906206811274472)
Monette A, Warren S, Barrett JC, Garnett-Benson C, Schalper KA, Taube JM, Topp B, Snyder A (2024) Biomarker development for PD-(L)1 axis inhibition: a consensus view from the SITC biomarkers committee. J Immunother Cancer 12:e009427. https://doi.org/10.1136/jitc-2024-009427. (PMID: 10.1136/jitc-2024-0094273903294311261685)
Monnier L, Cournède PH (2024) A novel batch-effect correction method for scRNA-seq data based on adversarial information factorization. PLoS Comput Biol 20(2):e1011880. https://doi.org/10.1371/journal.pcbi.1011880. (PMID: 10.1371/journal.pcbi.10118803838670010914288)
Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS (2024) From data to cure: a comprehensive exploration of multi-omics data analysis for targeted therapies. Mol Biotechnol. https://doi.org/10.1007/s12033-024-01133-6. (PMID: 10.1007/s12033-024-01133-63856577511928429)
Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS (2025) From data to cure: a comprehensive exploration of multi-omics data analysis for targeted therapies. Mol Biotechnol 67(4):1269–1289. https://doi.org/10.1007/s12033-024-01133-6. (PMID: 10.1007/s12033-024-01133-638565775)
Munir S, Ashfaq UA, Qasim M, Fatima T, Aslam S, Sarfraz MH, Kober AH, Khurshid M (2024) Molecular omics: a promising systems biology approach to unravel host-pathogen interactions. Systems biology approaches for host-pathogen interaction analysis. Academic Press, pp 81–102. https://doi.org/10.1016/B978-0-323-95890-5.00008-9. (PMID: 10.1016/B978-0-323-95890-5.00008-9)
Munoz-Barrera A, Rubio-Rodriguez LA, Jaspez D, Corrales A, Marcelino-Rodriguez I, Lorenzo-Salazar JM, Gonzalez-Montelongo R, Flores C (2024) Benchmarking of bioinformatics tools for the hybrid de novo assembly of human whole-genome sequencing data. Biorxiv. https://doi.org/10.1101/2024.05.28.595812. (PMID: 10.1101/2024.05.28.5958123967781311642750)
Naik K, Goyal RK, Foschini L, Chak CW, Thielscher C, Zhu H, Lu J, Lehár J, Pacanoswki MA, Terranova N, Mehta N, Korsbo N, Fakhouri T, Liu Q, Gobburu J (2024) Current status and future directions: the application of artificial intelligence/machine learning for precision medicine. Clin Pharm Ther 115:673–686. https://doi.org/10.1002/cpt.3152. (PMID: 10.1002/cpt.3152)
Nakase T, Guerra G, Ostrom Q, Ge T, Melin B, Wrensch M, Wiencke J, Jenkins R, Eckel-Passow J, Bondy M, Francis S, Kachuri L (2024) Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes. Neuro-Oncol. https://doi.org/10.1093/neuonc/noae112. (PMID: 10.1093/neuonc/noae1123891614011553268)
Nam Y, Kim J, Jung S-H, Woerner J, Suh EH, Lee D, Shivakumar M, Lee ME, Kim D (2024) Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci 7:225–250. https://doi.org/10.1146/annurev-biodatasci-102523-103801. (PMID: 10.1146/annurev-biodatasci-102523-1038013876839711972123)
Nekrutenko A, Taylor J (2012) Next-generation sequencing data interpretation: enhancing reproducibility and accessibility. Nat Rev Genet 13:667–672. https://doi.org/10.1038/nrg3305. (PMID: 10.1038/nrg330522898652)
Novakovsky G, Dexter N, Libbrecht M, Wasserman W, Mostafavi S (2022) Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet 24:125–137. https://doi.org/10.1038/s41576-022-00532-2. (PMID: 10.1038/s41576-022-00532-236192604)
O’Connor LM, O’Connor BA, Lim SB, Zeng J, Lo CH (2023) Integrative multi-omics and systems bioinformatics in translational neuroscience: a data mining perspective. J Pharm Anal, Special Issue: Single-Cell Spat Resolv Omics - II 13:836–850. https://doi.org/10.1016/j.jpha.2023.06.011. (PMID: 10.1016/j.jpha.2023.06.011)
Obaido G, Mienye ID, Egbelowo OF, Emmanuel ID, Ogunleye A, Ogbuokiri B, Mienye P, Aruleba K (2024) Supervised machine learning in drug discovery and development: algorithms, applications, challenges, and prospects. Mach Learn Appl 17:100576. https://doi.org/10.1016/j.mlwa.2024.100576. (PMID: 10.1016/j.mlwa.2024.100576)
Odenkirk MT, Reif DM, Baker ES (2021) Multiomic big data analysis challenges: increasing confidence in the interpretation of artificial intelligence assessments. Anal Chem 93(22):7763–7773. https://doi.org/10.1021/acs.analchem.0c04850. (PMID: 10.1021/acs.analchem.0c04850340290688465926)
Oh S, Geistlinger L, Ramos M, Blankenberg D, Van Den Beek M, Taroni J, Carey V, Greene C, Waldron L, Davis S (2022) Genomic supersignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases. Nat Commun. https://doi.org/10.1038/s41467-022-31411-3. (PMID: 10.1038/s41467-022-31411-3365662349789309)
Olawade DB, Clement David-Olawade A, Adereni T, Egbon E, Teke J, & Boussios S (2025) Integrating AI into cancer immunotherapy—A narrative review of current applications and future directions. Diseases, 13(1), 24.
Oliva A, Kaphle A, Reguant R, Sng LMF, Twine NA, Malakar Y, Wickramarachchi A, Keller M, Ranbaduge T, Chan EKF, Breen J, Buckberry S, Guennewig B, Haas M, Brown A, Cowley MJ, Thorne N, Jain Y, Bauer DC (2024) Future-proofing genomic data and consent management: a comprehensive review of technology innovations. Gigascience 13:giae021. https://doi.org/10.1093/gigascience/giae021. (PMID: 10.1093/gigascience/giae0213883794311152178)
Pantha N, Ramasubramanian M, Gurung I, Maskey M, Sanders LM, Casaletto J Costes SV (2024). Feature selection in high-dimensional space with applications to gene expression data. In: SoutheastCon 2024. Presented at the SoutheastCon 2024, pp. 6–15. https://doi.org/10.1109/SoutheastCon52093.2024.10500057 .
Pareek CS, Smoczynski R, Tretyn A (2011) Sequencing technologies and genome sequencing. J Appl Genet 52:413–435. https://doi.org/10.1007/s13353-011-0057-x. (PMID: 10.1007/s13353-011-0057-x216983763189340)
Peña-Martínez EG, Rodríguez-Martínez JA (2024) Decoding non-coding variants: recent approaches to studying their role in gene regulation and human diseases. Front Biosci-Schol 16:4. https://doi.org/10.31083/j.fbs1601004. (PMID: 10.31083/j.fbs1601004)
Peplow M (2016) The 100 000 Genomes Project. BMJ 353:i1757. https://doi.org/10.1136/bmj.i1757. (PMID: 10.1136/bmj.i175727075170)
Picard M, Scott-Boyer M-P, Bodein A, Périn O, Droit A (2021) Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 19:3735–3746. https://doi.org/10.1016/j.csbj.2021.06.030. (PMID: 10.1016/j.csbj.2021.06.030342857758258788)
Pinto Y, Bhatt AS (2024) Sequencing-based analysis of microbiomes. Nat Rev Genet 25(12):829–845. https://doi.org/10.1038/s41576-024-00746-6. (PMID: 10.1038/s41576-024-00746-638918544)
Podvalnyi A, Kopernik A, Sayganova M, Woroncow M, Zobkova G, Smirnova A, Albert E (2025) Quantitative analysis of pseudogene-associated errors during germline variant calling. Int J Mol Sci 26(1):363. https://doi.org/10.3390/ijms26010363. (PMID: 10.3390/ijms260103633979621911719938)
Ponnusamy S, Gupta P (2024) Scalable data partitioning techniques for distributed data processing in cloud environments: a review. IEEE Access 12:26735–26746. https://doi.org/10.1109/ACCESS.2024.3365810. (PMID: 10.1109/ACCESS.2024.3365810)
Pullin JM, McCarthy DJ (2024) A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol 25(1):56. https://doi.org/10.1186/s13059-024-03183-0. (PMID: 10.1186/s13059-024-03183-03840905610895860)
Pun F, Ozerov I, Zhavoronkov A (2023) AI-powered therapeutic target discovery. Trends Pharmacol Sci. https://doi.org/10.1016/j.tips.2023.06.010. (PMID: 10.1016/j.tips.2023.06.01037479540)
Qin Y, Huo M, Liu X, Li SC (2024) Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol 15:1368749. https://doi.org/10.3389/fimmu.2024.1368749. (PMID: 10.3389/fimmu.2024.13687493852413510957591)
Qiu Y, Cheng F (2024) Artificial intelligence for drug discovery and development in Alzheimer’s disease. Curr Opin Struct Biol 85:102776. https://doi.org/10.1016/j.sbi.2024.102776. (PMID: 10.1016/j.sbi.2024.10277638335558)
Qiu X, Li H, Ver Steeg G, Godzik A (2024) Advances in AI for protein structure prediction: implications for cancer drug discovery and development. Biomolecules 14:339. https://doi.org/10.3390/biom14030339. (PMID: 10.3390/biom140303393854075910968151)
Quazi S (2022) Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 39(8):120. (PMID: 10.1007/s12032-022-01711-1357041529198206)
Rai T, Saji A, Bisa S, Vasudevan K, Rambabu M, Dasegowda KR (2025) Deep learning insights into transcriptomics and gene expression patterns analysis. Deep learning in genetics and genomics. Academic Press, pp 47–59. https://doi.org/10.1016/B978-0-443-27523-4.00016-0. (PMID: 10.1016/B978-0-443-27523-4.00016-0)
Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) AI in health and medicine. Nat Med 28(1):31–38. https://doi.org/10.1038/s41591-021-01614-0. (PMID: 10.1038/s41591-021-01614-035058619)
Reel P, Reel S, Pearson E, Trucco E, Jefferson E (2021) Using machine learning approaches for multi-omics data analysis: a review. Biotechnol Adv. https://doi.org/10.1016/j.biotechadv.2021.107739. (PMID: 10.1016/j.biotechadv.2021.10773933794304)
Rehman A, Mujahid M, Saba T, Jeon G (2024) Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry. Funct Integr Genomics 24:1–17. https://doi.org/10.1007/s10142-024-01289-z. (PMID: 10.1007/s10142-024-01289-z)
Robishaw JD, Alter SM, Solano JJ, Shih RD, DeMets DL, Maki DG, & Hennekens CH (2021) Genomic surveillance to combat COVID-19: challenges and opportunities. The Lancet Microbe 2(9):e481-e484.
Sagharchi SMAM, Sheykhhasan M, Ghorbani A, Afrazeh E, Poondla N, Kalhor N, Tanzadehpanah H, Mahaki H, Manoochehri H (2025) Genomics: a piece of the multi-omics puzzle. Effective techniques for bioinformatic exploration. IGI Global, pp 23–68. https://doi.org/10.4018/979-8-3693-3192-7.ch002. (PMID: 10.4018/979-8-3693-3192-7.ch002)
Sahu M, Gupta R, Ambasta RK, Kumar P (2022) Artificial intelligence and machine learning in precision medicine: a paradigm shift in big data analysis. Prog Mol Biol Transl Sci 190(1):57–100. https://doi.org/10.1016/bs.pmbts.2022.03.002. (PMID: 10.1016/bs.pmbts.2022.03.00236008002)
Sanches PHG, de Melo NC, Porcari AM, de Carvalho LM (2024) Integrating molecular perspectives: strategies for comprehensive multi-omics integrative data analysis and machine learning applications in transcriptomics, proteomics, and metabolomics. Biology 13(11):848. https://doi.org/10.3390/biology13110848. (PMID: 10.3390/biology131108483959680311592251)
Saravanan TR, Poongothai E, Sophia NA, Jeyanthi S, Kanimozhi N, Boopathi S (2025) Deep learning in bioinformatics for revolutionizing genomic data analysis. Advanced interdisciplinary applications of deep learning for data science. IGI Global Scientific Publishing, pp 1–30. https://doi.org/10.4018/979-8-3693-4759-1.ch00. (PMID: 10.4018/979-8-3693-4759-1.ch00)
Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare RP, Banday S, Mishra AK, Das G, Malonia SK (2023) Next-generation sequencing technology: current trends and advancements. Biology 12(7):997. https://doi.org/10.3390/biology12070997. (PMID: 10.3390/biology120709973750842710376292)
Scarano C, Veneruso I, De Simone RR, Di Bonito G, Secondino A, & D’Argenio V (2024) The third-generation sequencing challenge: novel insights for the omic sciences. Biomol 14(5):568.
Schäfer PSL, Dimitrov D, Villablanca EJ, Saez-Rodriguez J (2024) Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nat Immunol 25:405–417. https://doi.org/10.1038/s41590-024-01768-2. (PMID: 10.1038/s41590-024-01768-238413722)
Schauperl M, Denny R (2022) AI-based protein structure prediction in drug discovery: impacts and challenges. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.2c00026. (PMID: 10.1021/acs.jcim.2c0002635727311)
Seem K, Kaur S, Kumar S, Mohapatra T (2024) Epigenome editing for targeted DNA (de)methylation: a new perspective in modulating gene expression. Crit Rev Biochem Mol Biol 59:69–98. https://doi.org/10.1080/10409238.2024.2320659. (PMID: 10.1080/10409238.2024.232065938440883)
Serrano D, Luciano F, Anaya B, Ongoren B, Kara A, Molina G, Ramirez B, Sánchez-Guirales S, Simon J, Tomietto G, Rapti C, Ruiz H, Rawat S, Kumar D, Lalatsa A (2024) Artificial intelligence (AI) applications in drug discovery and drug delivery: revolutionizing personalized medicine. Pharmaceutics. https://doi.org/10.3390/pharmaceutics16101328. (PMID: 10.3390/pharmaceutics161013283959858411597491)
Sforzini L, Marizzoni M, Bottanelli C, Kunšteková V, Zonca V, Saleri S, Kose M, Lombardo G, Mariani N, Nettis M, Nikkheslat N, Worrell C, Zajkowska Z, Pointon L, Cowen P, Cavanagh J, Harrison N, Riva M, Mondelli V, Bullmore E, Cattaneo A, Pariante C (2024) Transcriptomic profiles in major depressive disorder: the role of immunometabolic and cell-cycle-related pathways in depression with different levels of inflammation. Mol Psychiatry. https://doi.org/10.1038/s41380-024-02736-w. (PMID: 10.1038/s41380-024-02736-w3927175411919688)
Sherani AMK, Khan M, Qayyum MU, Hussain HK (2024) Synergizing AI and healthcare: pioneering advances in cancer medicine for personalized treatment. Int J Multidiscip Sci Arts 3:270–277. https://doi.org/10.47709/ijmdsa.v3i01.3562. (PMID: 10.47709/ijmdsa.v3i01.3562)
Shi C, Cheng L, Yu Y, Chen S, Dai Y, Yang J, Zhang H, Chen J, Geng N (2024) Multi-omics integration analysis: tools and applications in environmental toxicology. Environ Pollut 360:124675. https://doi.org/10.1016/j.envpol.2024.124675. (PMID: 10.1016/j.envpol.2024.12467539103035)
Shukla V, Singh S, Verma S, Verma S, Rizvi AA, Abbas M (2024) Targeting the microbiome to improve human health with the approach of personalized medicine: latest aspects and current updates. Clin Nutr ESPEN. https://doi.org/10.1016/j.clnesp.2024.08.005. (PMID: 10.1016/j.clnesp.2024.08.00539178987)
Sigala RE, Lagou V, Shmeliov A, Atito S, Kouchaki S, Awais M, Prokopenko I, Mahdi A, Demirkan A (2023) Machine learning to advance human genome-wide association studies. Genes 15(1):34. https://doi.org/10.3390/genes15010034. (PMID: 10.3390/genes150100343825492410815885)
Singh S, Gupta H, Sharma P, Sahi S (2024) Advances in artificial intelligence (AI)-assisted approaches in drug screening. Artif Intell Chem 2(1):100039. https://doi.org/10.1016/j.aichem.2023.100039. (PMID: 10.1016/j.aichem.2023.100039)
Sinha P, Singh VK, Bohra A, Kumar A, Reif JC, Varshney RK (2021) Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits. Theor Appl Genet 134(6):1829–1843. https://doi.org/10.1007/s00122-021-03847-6. (PMID: 10.1007/s00122-021-03847-6340143738205890)
Sousa V, Hey J (2013) Understanding the origin of species with genome-scale data: modelling gene flow. Nat Rev Genet 14:404–414. https://doi.org/10.1038/nrg3446. (PMID: 10.1038/nrg3446236574795568773)
Sowmya SV, Augustine D, Mushtaq S, Baeshen HA, Ashi H, Hassan RN, Alshahrani M, Patil S (2024) Revitalizing oral cancer research: crispr-cas9 technology the promise of genetic editing. Front Oncol 14:1383062. https://doi.org/10.3389/fonc.2024.1383062. (PMID: 10.3389/fonc.2024.1383062)
Steyaert S, Pizurica M, Nagaraj D, Khandelwal P, Hernandez-Boussard T, Gentles AJ, Gevaert O (2023) Multimodal data fusion for cancer biomarker discovery with deep learning. Nat Mach Intell 5:351–362. https://doi.org/10.1038/s42256-023-00633-5. (PMID: 10.1038/s42256-023-00633-53769385210484010)
Subramanian I, Verma S, Kumar S, Jere A, Anamika K (2020) Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. https://doi.org/10.1177/1177932219899051. (PMID: 10.1177/1177932219899051320763697003173)
Sun K, Roy A, Tobin JM (2024) Artificial intelligence and machine learning: definition of terms and current concepts in critical care research. J Crit Care 82:154792. https://doi.org/10.1016/j.jcrc.2024.154792. (PMID: 10.1016/j.jcrc.2024.15479238554543)
Tuggle CK, Clarke JL, Murdoch BM, Lyons E, Scott NM, Beneš B, Schnable PS (2024) Current challenges and future of agricultural genomes to phenomes in the USA. Genome biology, 25(1), 8.
Taherdoost H, Ghofrani A (2024) AI and the evolution of personalized medicine in pharmacogenomics. Intell Pharm. https://doi.org/10.1016/j.ipha.2024.08.005. (PMID: 10.1016/j.ipha.2024.08.005)
Tang B, He F, Liu D, He F, Wu T, Fang M, Niu Z, Wu Z, Xu D (2022) AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. Biomolecules. https://doi.org/10.3390/biom12060746. (PMID: 10.3390/biom12060746366714539855424)
Tanjo T, Kawai Y, Tokunaga K, Ogasawara O, Nagasaki M (2021) Practical guide for managing large-scale human genome data in research. J Hum Genet 66(1):39–52. https://doi.org/10.1038/s10038-020-00862-1. (PMID: 10.1038/s10038-020-00862-133097812)
Taqi M, Younus S, Yousaf A (2025) Role of artificial intelligence in genomics. Int J Res Publ Rev. https://doi.org/10.55248/gengpi.6.0125.0509. (PMID: 10.55248/gengpi.6.0125.0509)
Terranova N, Venkatakrishnan K (2024) Machine learning in modeling disease trajectory and treatment outcomes: an emerging enabler for model-informed precision medicine. Clin Pharma Ther 115:720–726. https://doi.org/10.1002/cpt.3153. (PMID: 10.1002/cpt.3153)
Terranova N, Renard D, Shahin MH, Menon S, Cao Y, Hop CECA, Hayes S, Madrasi K, Stodtmann S, Tensfeldt T, Vaddady P, Ellinwood N, Lu J (2024) Artificial intelligence for quantitative modeling in drug discovery and development: an innovation and quality consortium perspective on use cases and best practices. Clin Pharm Ther 115:658–672. https://doi.org/10.1002/cpt.3053. (PMID: 10.1002/cpt.3053)
Thangi DS (2025) Genomic data. Computational intelligence for genomics data. Academic Press, pp 3–16. https://doi.org/10.1016/B978-0-443-30080-6.00002-X. (PMID: 10.1016/B978-0-443-30080-6.00002-X)
Tiong K-L, Luzhbin D, Yeang C-H (2024) Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data. BMC Bioinformatics 25:209. https://doi.org/10.1186/s12859-024-05825-3. (PMID: 10.1186/s12859-024-05825-33886719311167951)
Tong X, Qu N, Kong X, Ni S, Zhou J, Wang K, Zhang L, Wen Y, Shi J, Zhang S, Li X, Zheng M (2024) Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery. Nat Commun 15:5378. https://doi.org/10.1038/s41467-024-49620-3. (PMID: 10.1038/s41467-024-49620-33891836911199551)
Toni E, Ayatollahi H, Abbaszadeh R, Siahpirani A (2024) Machine learning techniques for predicting drug-related side effects: a scoping review. Pharmaceuticals. https://doi.org/10.3390/ph17060795. (PMID: 10.3390/ph170607953893146211206653)
Toufikuzzaman M, Hassan Samee MA, Sohel Rahman M (2024) Crispr-dipoff: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction. Brief Bioinform 25:bbad530. https://doi.org/10.1093/bib/bbad530. (PMID: 10.1093/bib/bbad5303838868010883906)
Tran D, Nguyen H, Pham VD, Nguyen P, Nguyen Luu H, Minh Phan L, Nguyen T (2025) A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables. Brief Bioinform 26(2):bbaf150. https://doi.org/10.1093/bib/bbaf150. (PMID: 10.1093/bib/bbaf1504022195911994034)
Ulhassan M, Ahmed A, Abbas A, Qazi T, Hadid F, Malik Z, Khan FS (2025) Deconstructing the one-size-fits-all model: a systematic review of personalized acute care strategies. Rev J Neurol Med Sci Rev 3(2):1–15. https://doi.org/10.62019/0423f439. (PMID: 10.62019/0423f439)
Upadhyaya S, Danilevicz M, Dolatabadian A, Neik T, Zhang F, Al-Mamun H, Bennamoun M, Batley J, Edwards D (2024) Genomics-based plant disease resistance prediction using machine learning. Plant Pathol. https://doi.org/10.1111/ppa.13988. (PMID: 10.1111/ppa.13988)
Uriawan W, Adriansyah S, Maulidiyah SJ, Julianto S, Jamil WS (2024). Challenges and opportunities: improve patient data security and privacy in distributed systems. https://doi.org/10.20944/preprints202407.0163.v1 .
Uzoeto HO, Cosmas S, Bakare TT, Durojaye OA (2024) AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements. Beni-Suef Univ J Basic Appl Sci 13:46. https://doi.org/10.1186/s43088-024-00503-y. (PMID: 10.1186/s43088-024-00503-y)
Valous NA, Popp F, Zörnig I, Jäger D, Charoentong P (2024) Graph machine learning for integrated multi-omics analysis. Br J Cancer 131:205–211. https://doi.org/10.1038/s41416-024-02706-7. (PMID: 10.1038/s41416-024-02706-73872999611263675)
Vavekanand R (2024) Data security and privacy in genomics research: a comparative analysis to protect confidentiality. SMHS 1:23–31. https://doi.org/10.48185/smhs.v1i1.1158. (PMID: 10.48185/smhs.v1i1.1158)
Verma A, Sharma T, Awasthi A (2024) CRISPR and gene editing: A game-changer in drug development. Current Pharmaceutical Design, 30(15), 1133-1135.
Verma D, Kapoor S (2024) Omics approach for personalized and diagnostics medicine. Integrative omics. Elsevier, pp 175–185. https://doi.org/10.1016/B978-0-443-16092-9.00011-4. (PMID: 10.1016/B978-0-443-16092-9.00011-4)
Vitale I, Shema E, Loi S, Galluzzi L (2021) Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nat Med 27(2):212–224. https://doi.org/10.1038/s41591-021-01233-9. (PMID: 10.1038/s41591-021-01233-933574607)
Wen J, Zhang Z, Lan Y, Cui Z, Cai J, Zhang W (2023) A survey on federated learning: challenges and applications. Int J Mach Learn Cybern 14(2):513–535. https://doi.org/10.1007/s13042-022-01647-y. (PMID: 10.1007/s13042-022-01647-y36407495)
Williamson SM, Prybutok V (2024) Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci 14(2):675. https://doi.org/10.3390/app14020675. (PMID: 10.3390/app14020675)
Wu Y, Xie L (2025) AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships. Comput Struct Biotechnol J. https://doi.org/10.1016/j.csbj.2024.12.030. (PMID: 10.1016/j.csbj.2024.12.0304134089212671354)
Xu J, Yang P, Xue S, Sharma B, Sanchez-Martin M, Wang F, Beaty K, Dehan E, Parikh B (2019) Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet 138:109–124. https://doi.org/10.1007/s00439-019-01970-5. (PMID: 10.1007/s00439-019-01970-5306716726373233)
Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J (2021) Artificial intelligence: a powerful paradigm for scientific research. Innov (Camb) 2(4):100179.
Xu X, Li J, Zhu Z, Zhao L, Wang H, Song C, Chen Y, Zhao Q, Yang J, Pei Y (2024) A comprehensive review on synergy of multi-modal data and AI technologies in medical diagnosis. Bioengineering 11(3):219. https://doi.org/10.3390/bioengineering11030219. (PMID: 10.3390/bioengineering110302193853449310967767)
Yan T, Yue Z, Xu H, Liu Y, Hong Y, Chen G, Tao L, Xie T (2022) A systematic review of state-of-the-art strategies for machine learning-based protein function prediction. Comput Biol Med 154:106446. https://doi.org/10.1016/j.compbiomed.2022.106446. (PMID: 10.1016/j.compbiomed.2022.10644636680931)
Yang Y, Zhao Y, Liu X, Huang J (2022) Artificial intelligence for prediction of response to cancer immunotherapy. Semin Cancer Biol. https://doi.org/10.1016/j.semcancer.2022.11.008. (PMID: 10.1016/j.semcancer.2022.11.0083652173710237282)
Yang X, Huang K, Yang D, Zhao W, Zhou X (2024) Biomedical big data technologies, applications, and challenges for precision medicine: a review. Glob Chall 8:2300163. https://doi.org/10.1002/gch2.202300163. (PMID: 10.1002/gch2.20230016338223896)
Ye J, Woods D, Jordan N, Starren J (2024) The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA Summits Transl Sci Proc 2024:459. (PMID: 3882706111141850)
Yin F, Shi F (2022) A comparative survey of big data computing and HPC: from a parallel programming model to a cluster architecture. Int J Parallel Program 50(1):27–64. https://doi.org/10.1007/s10766-021-00717-y. (PMID: 10.1007/s10766-021-00717-y)
Yingngam B (2024) Introduction to bioinformatics. In: Khanna A, El Barachi M, Jain S, Kumar M, Nayyar A (eds) artificial intelligence and machine learning in drug design and development. Wiley, pp 23–66. https://doi.org/10.1002/9781394234196.ch2. (PMID: 10.1002/9781394234196.ch2)
Youvan D (2024). Advancements in protein structure prediction: from AlphaFold’s breakthroughs to implications in drug discovery and viral interaction. https://doi.org/10.13140/RG.2.2.20858.91841 .
Yue T, Wang Y, Zhang L, Gu C, Xue H, Wang W, Lyu Q, Dun Y (2023) Deep learning for genomics: from early neural nets to modern large language models. Int J Mol Sci. https://doi.org/10.3390/ijms242115858. (PMID: 10.3390/ijms2421158583813910710744249)
Zafar I, Anwar S, kanwal F, Yousaf W, Un Nisa F, Kausar T, ul Ain Q, Unar A, Kamal MA, Rashid S, Khan KA, Sharma R (2023) Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine. Biomed Signal Process Control 86:105263. https://doi.org/10.1016/j.bspc.2023.105263. (PMID: 10.1016/j.bspc.2023.105263)
Zhang C, Yang J, Chen S, Sun L, Li K, Lai G, Peng B, Zhong X, Xie B (2024a) Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling. EPMA J 15(3):525–544. https://doi.org/10.1007/s13167-024-00374-4. (PMID: 10.1007/s13167-024-00374-43923910911371997)
Zhang J, Lang M, Zhou Y, Zhang Y (2024b) Predicting RNA structures and functions by artificial intelligence. Trends Genet 40(1):94–107. https://doi.org/10.1016/j.tig.2023.10.001. (PMID: 10.1016/j.tig.2023.10.001)
Zhao Y, Wong L, Goh WWB (2020) How to do quantile normalization correctly for gene expression data analyses. Sci Rep 10(1):15534. https://doi.org/10.1038/s41598-020-72664-6. (PMID: 10.1038/s41598-020-72664-6329681967511327)
Zhao Z, Gruenloh T, Yan M, Wu Y, Sun Z, Miao J, Lu Q (2024) Optimizing and benchmarking polygenic risk scores with GWAS summary statistics. Genome Biol 25(1):260. https://doi.org/10.1186/s13059-024-03400-w. (PMID: 10.1186/s13059-024-03400-w3937999911462675)
Zhou J, Chen S, Wu Y, Li H, Zhang B, Zhou L, Hu Y, Xiang Z, Li Z, Chen N, Han W, Xu C, Wang D, Gao X (2024) PPML-omics: a privacy-preserving federated machine learning method protects patients’ privacy in omic data. Sci Adv 10:eadh8601. https://doi.org/10.1126/sciadv.adh8601. (PMID: 10.1126/sciadv.adh86013829517810830108)
Zhu J, Wang J, Wang X, Gao M, Guo B, Gao M, Liu J, Yu Y, Wang L, Kong W, An Y, Liu Z, Sun X, Huang Z, Zhou H, Zhang N, Zheng R, Xie Z (2021) Prediction of drug efficacy from transcriptional profiles with deep learning. Nat Biotechnol 39:1444–1452. https://doi.org/10.1038/s41587-021-00946-z. (PMID: 10.1038/s41587-021-00946-z34140681)
Zhu Y, Bai L, Ning Z, Fu W, Liu J, Jiang L, Fei S, Gong S, Lu L, Deng M, Yi M (2024) Deep learning for clustering single-cell RNA-seq data. Curr Bioinform 19:193–210. https://doi.org/10.2174/1574893618666221130094050. (PMID: 10.2174/1574893618666221130094050)
Zimmermann MT (2018) The importance of biologic knowledge and gene expression context for genomic data interpretation. Front Genet 9:670. https://doi.org/10.3389/fgene.2018.00670. (PMID: 10.3389/fgene.2018.00670306194866305277)
Zou Y, Zhao Z, Song Y (2024) An overview of multiomics: a powerful tool applied in cancer molecular subtyping for cancer therapy. Malig Spectr 1:15–29. https://doi.org/10.1002/msp2.16. (PMID: 10.1002/msp2.16)

Keywords: Artificial intelligence; Deep learning; Genomic data analysis; Machine learning; Multi-omics integration; Personalized medicine

Date Created: 20251205 Date Completed: 20251205 Latest Revision: 20251205

20251205

10.1007/s00438-025-02314-x

41348251

MEDLINE