Author:
Ahmed Zeeshan,Wan Shibiao,Zhang Fan,Zhong Wen
Abstract
AbstractRecent technological advancements have vastly improved access to high-throughput biological instrumentation, sparking an unparalleled surge in omics data generation. The implementation of artificial intelligence techniques is revolutionizing omics data interpretation. The BMC Methods Collection "Artificial intelligence for omics data analysis" will feature novel artificial intelligence approaches leveraging multi-omics data to accelerate discoveries in personalized medicine, disease diagnostics, drug development, and biological pathway elucidation.
Publisher
Springer Science and Business Media LLC
Reference13 articles.
1. Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: a review. Biotechnol Adv. 2021;49:107739. https://doi.org/10.1016/j.biotechadv.2021.107739.
2. Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019;20(8):467–84. https://doi.org/10.1038/s41576-019-0127-1.
3. Chen C, Wang J, Pan D, et al. Applications of multi-omics analysis in human diseases. MedComm (2020). 2023;4(4):e315. https://doi.org/10.1002/mco2.315. Published 2023 Jul 31.
4. McCarthy J, Minsky M, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence. AI Mag. 2006;27(4):12–4.
5. Li R, Li L, Xu Y, Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform. 2022;23(1):bbab460.
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