This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.
References
[1]
Nabi, A., Dilekoglu, B., Adebali, O. and Tastan, O. (2022) Discovering Misannotated Lncrnas Using Deep Learning Training Dynamics. Bioinformatics, 39, btac821. https://doi.org/10.1093/bioinformatics/btac821
[2]
Chen, J., Shrestha, L., Green, G., Leier, A. and Marquez-Lago, T.T. (2023) The Hitchhikers’ Guide to RNA Sequencing and Functional Analysis. Briefings in Bioinformatics, 24, bbac529. https://doi.org/10.1093/bib/bbac529
[3]
Kim, G.B., Kim, J.Y., Lee, J.A., Norsigian, C.J., Palsson, B.O. and Lee, S.Y. (2023) Functional Annotation of Enzyme-Encoding Genes Using Deep Learning with Transformer Layers. Nature Communications, 14, Article No. 7370. https://doi.org/10.1038/s41467-023-43216-z
[4]
Xu, H., Wang, S., Fang, M., Luo, S., Chen, C., Wan, S., et al. (2023) SPACEL: Deep Learning-Based Characterization of Spatial Transcriptome Architectures. Nature Communications, 14, Article No. 7603. https://doi.org/10.1038/s41467-023-43220-3
[5]
Zhu, Y., Zhang, C., Yu, D. and Zhang, Y. (2022) Integrating Unsupervised Language Model with Triplet Neural Networks for Protein Gene Ontology Prediction. PLOS Computational Biology, 18, e1010793. https://doi.org/10.1371/journal.pcbi.1010793
[6]
Nguyen, A., Vasilaki, S. and Martínez, M.R. (2023) FLAN: Feature-Wise Latent Additive Neural Models for Biological Applications. Briefings in Bioinformatics, 24, bbad056. https://doi.org/10.1093/bib/bbad056
[7]
Zhapa-Camacho, F., Kulmanov, M. and Hoehndorf, R. (2022) Mowl: Python Library for Machine Learning with Biomedical Ontologies. Bioinformatics, 39, btac811. https://doi.org/10.1093/bioinformatics/btac811
[8]
Zhang, Y., Wang, H., Liu, J., Li, J., Zhang, Q., Tang, B., et al. (2023) Delta.EPI: A Probabilistic Voting-Based Enhancer-Promoter Interaction Prediction Platform. Journal of Genetics and Genomics, 50, 519-527. https://doi.org/10.1016/j.jgg.2023.02.006
[9]
Kim, G.B., Gao, Y., Palsson, B.O. and Lee, S.Y. (2020) DeepTFactor: A Deep Learning-Based Tool for the Prediction of Transcription Factors. Proceedings of the National Academy of Sciences, 118, e2021171118. https://doi.org/10.1073/pnas.2021171118
Birkenbihl, C., Ahmad, A., Massat, N.J., Raschka, T., Avbersek, A., Downey, P., et al. (2023) Artificial Intelligence-Based Clustering and Characterization of Parkinson’s Disease Trajectories. Scientific Reports, 13, Article No. 2897. https://doi.org/10.1038/s41598-023-30038-8
[12]
Keyl, P., Bischoff, P., Dernbach, G., Bockmayr, M., Fritz, R., Horst, D., et al. (2023) Single-Cell Gene Regulatory Network Prediction by Explainable AI. Nucleic Acids Research, 51, e20-e20. https://doi.org/10.1093/nar/gkac1212
[13]
Libiseller-Egger, J., Phelan, J.E., Attia, Z.I., Benavente, E.D., Campino, S., Friedman, P.A., et al. (2022) Deep Learning-Derived Cardiovascular Age Shares a Genetic Basis with Other Cardiac Phenotypes. Scientific Reports, 12, Article No. 22265. https://doi.org/10.1038/s41598-022-27254-z
[14]
Mathis, N., Allam, A., Kissling, L., Marquart, K.F., Schmidheini, L., Solari, C., et al. (2023) Predicting Prime Editing Efficiency and Product Purity by Deep Learning. Nature Biotechnology, 41, 1151-1159. https://doi.org/10.1038/s41587-022-01613-7
[15]
Sha, G. and Li, G. (2023) Effector Translocation and Rational Design of Disease Resistance. Trends in Microbiology, 31, 1202-1205. https://doi.org/10.1016/j.tim.2023.09.007
[16]
Stolfi, P., Mastropietro, A., Pasculli, G., Tieri, P. and Vergni, D. (2023) NIAPU: Network-Informed Adaptive Positive-Unlabeled Learning for Disease Gene Identification. Bioinformatics, 39, btac848. https://doi.org/10.1093/bioinformatics/btac848
[17]
Kabir, M., Stuart, H.M., Lopes, F.M., Fotiou, E., Keavney, B., Doig, A.J., et al. (2023) Predicting Congenital Renal Tract Malformation Genes Using Machine Learning. Scientific Reports, 13, Article No. 13204. https://doi.org/10.1038/s41598-023-38110-z
[18]
Srivastava, R. (2022) Applications of Artificial Intelligence Multiomics in Precision Oncology. Journal of Cancer Research and Clinical Oncology, 149, 503-510. https://doi.org/10.1007/s00432-022-04161-4
[19]
Rosenski, J., Shifman, S. and Kaplan, T. (2023) Predicting Gene Knockout Effects from Expression Data. BMC Medical Genomics, 16, Article No. 26. https://doi.org/10.1186/s12920-023-01446-6
[20]
Gan, Y., Liu, W., Xu, G., Yan, C. and Zou, G. (2023) DMFDDI: Deep Multimodal Fusion for Drug-Drug Interaction Prediction. Briefings in Bioinformatics, 24, bbad397. https://doi.org/10.1093/bib/bbad397
[21]
Banu, A., Ahmed, R., Musleh, S., Shah, Z., Househ, M. and Alam, T. (2023) Predicting Overall Survival in METABRIC Cohort Using Machine Learning. In: Studies in Health Technology and Informatics, IOS Press, 632-635. https://doi.org/10.3233/shti230577
[22]
Smith, G.D., Ching, W.H., Cornejo-Páramo, P. and Wong, E.S. (2023) Decoding Enhancer Complexity with Machine Learning and High-Throughput Discovery. Genome Biology, 24, Article No. 116. https://doi.org/10.1186/s13059-023-02955-4
[23]
Cheng, N., Liu, J., Chen, C., Zheng, T., Li, C. and Huang, J. (2023) Prediction of Lung Cancer Metastasis by Gene Expression. Computers in Biology and Medicine, 153, Article 106490. https://doi.org/10.1016/j.compbiomed.2022.106490
[24]
Shao, J., Ma, J., Zhang, Q., Li, W. and Wang, C. (2023) Predicting Gene Mutation Status via Artificial Intelligence Technologies Based on Multimodal Integration (MMI) to Advance Precision Oncology. Seminars in Cancer Biology, 91, 1-15. https://doi.org/10.1016/j.semcancer.2023.02.006
[25]
Park, M., Lim, J., Jeong, J., Jang, Y., Lee, J., Lee, J., et al. (2022) Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration. Biomolecules, 12, Article 1839. https://doi.org/10.3390/biom12121839
[26]
Zhuang, Y., Xing, F., Ghosh, D., Hobbs, B.D., Hersh, C.P., Banaei-Kashani, F., et al. (2023) Deep Learning on Graphs for Multi-Omics Classification of COPD. PLOS ONE, 18, e0284563. https://doi.org/10.1371/journal.pone.0284563
[27]
Coleman, K., Hu, J., Schroeder, A., Lee, E.B. and Li, M. (2023) Spadecon: Cell-Type Deconvolution in Spatial Transcriptomics with Semi-Supervised Learning. Communications Biology, 6, Article No. 378. https://doi.org/10.1038/s42003-023-04761-x
[28]
Knudsen, J.E., Rich, J.M. and Ma, R. (2024) Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma. Urologic Clinics of North America, 51, 47-62. https://doi.org/10.1016/j.ucl.2023.06.002
[29]
Mirza, Z., Ansari, M.S., Iqbal, M.S., Ahmad, N., Alganmi, N., Banjar, H., et al. (2023) Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis. Cancers, 15, Article 3237. https://doi.org/10.3390/cancers15123237
[30]
Wang, X., Meng, L., Zhang, J., Zhao, Z., Zou, L., Jia, Z., et al. (2023) Identification of Ferroptosis-Related Molecular Clusters and Genes for Diabetic Osteoporosis Based on the Machine Learning. Frontiers in Endocrinology, 14, Article 1189513. https://doi.org/10.3389/fendo.2023.1189513
[31]
Li, W., Guo, J., Chen, J., Yao, H., Mao, R., Li, C., et al. (2022) Identification of Immune Infiltration and the Potential Biomarkers in Diabetic Peripheral Neuropathy through Bioinformatics and Machine Learning Methods. Biomolecules, 13, Article 39. https://doi.org/10.3390/biom13010039
[32]
Chen, W., Yang, Q., Hu, L., Wang, M., Yang, Z., Zeng, X., et al. (2023) Shared Diagnostic Genes and Potential Mechanism between PCOS and Recurrent Implantation Failure Revealed by Integrated Transcriptomic Analysis and Machine Learning. Frontiers in Immunology, 14, Article 1175384. https://doi.org/10.3389/fimmu.2023.1175384
[33]
Xing, N., Dong, Z., Wu, Q., Zhang, Y., Kan, P., Han, Y., et al. (2023) Identification of Ferroptosis Related Biomarkers and Immune Infiltration in Parkinson’s Disease by Integrated Bioinformatic Analysis. BMC Medical Genomics, 16, Article No, 55. https://doi.org/10.1186/s12920-023-01481-3
[34]
Cai, L., Tang, S., Liu, Y., Zhang, Y. and Yang, Q. (2023) The Application of Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Screening of Parkinson’s Disease Biomarkers and Construction of Diagnostic Models. Frontiers in Molecular Neuroscience, 16, Article 1274268. https://doi.org/10.3389/fnmol.2023.1274268
[35]
Lai, Y., Lin, P., Lin, F., Chen, M., Lin, C., Lin, X., et al. (2022) Identification of Immune Microenvironment Subtypes and Signature Genes for Alzheimer’s Disease Diagnosis and Risk Prediction Based on Explainable Machine Learning. Frontiers in Immunology, 13, Article 1046410. https://doi.org/10.3389/fimmu.2022.1046410
[36]
Zhao, X., Duan, L., Cui, D. and Xie, J. (2023) Exploration of Biomarkers for Systemic Lupus Erythematosus by Machine-Learning Analysis. BMC Immunology, 24, Article No. 44. https://doi.org/10.1186/s12865-023-00581-0
[37]
Gao, Q., Jin, H., Xu, W. and Wang, Y. (2023) Predicting Diagnostic Gene Biomarkers in Patients with Diabetic Kidney Disease Based on Weighted Gene Co Expression Network Analysis and Machine Learning Algorithms. Medicine, 102, e35618. https://doi.org/10.1097/md.0000000000035618
[38]
Cong, D., Zhao, Y., Zhang, W., Li, J. and Bai, Y. (2023) Applying Machine Learning Algorithms to Develop a Survival Prediction Model for Lung Adenocarcinoma Based on Genes Related to Fatty Acid Metabolism. Frontiers in Pharmacology, 14, Article 1260742. https://doi.org/10.3389/fphar.2023.1260742
[39]
Chen, G., He, Z., Jiang, W., Li, L., Luo, B., Wang, X., et al. (2022) Construction of a Machine Learning-Based Artificial Neural Network for Discriminating Panoptosis Related Subgroups to Predict Prognosis in Low-Grade Gliomas. Scientific Reports, 12, Article No. 22219. https://doi.org/10.1038/s41598-022-26389-3
[40]
Bao, W., Wang, L., Liu, X. and Li, M. (2023) Predicting Diagnostic Biomarkers Associated with Immune Infiltration in Crohn’s Disease Based on Machine Learning and Bioinformatics. European Journal of Medical Research, 28, Article No. 255. https://doi.org/10.1186/s40001-023-01200-9
[41]
Liu, J., Wu, P., Lai, S., Wang, J., Wang, J. and Zhang, Y. (2023) Identifying Possible Hub Genes and Biological Mechanisms Shared between Bladder Cancer and Inflammatory Bowel Disease Using Machine Learning and Integrated Bioinformatics. Journal of Cancer Research and Clinical Oncology, 149, 16885-16904. https://doi.org/10.1007/s00432-023-05266-0
[42]
Zhang, H., Yan, C., Xia, Y., Guan, J. and Zhou, S. (2023) Causal Gene Identification Using Non-Linear Regression-Based Independence Tests. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20, 185-195. https://doi.org/10.1109/tcbb.2022.3149864
[43]
Zhao, H., Souilljee, M., Pavlidis, P. and Alachiotis, N. (2023) Genome-Wide Scans for Selective Sweeps Using Convolutional Neural Networks. Bioinformatics, 39, i194-i203. https://doi.org/10.1093/bioinformatics/btad265
[44]
Wang, Z., Liang, S., Liu, S., Meng, Z., Wang, J. and Liang, S. (2023) Sequence Pre-Training-Based Graph Neural Network for Predicting lncRNA-miRNA Associations. Briefings in Bioinformatics, 24, bbad317. https://doi.org/10.1093/bib/bbad317
[45]
Muthamilselvan, S., Ramasami Sundhar Baabu, P. and Palaniappan, A. (2023) Microfluidics for Profiling Mirna Biomarker Panels in AI-Assisted Cancer Diagnosis and Prognosis. Technology in Cancer Research & Treatment, 22, Article 15330338231185284. https://doi.org/10.1177/15330338231185284