Abstract
Multi-omics data from genomics representing molecular, metabolomic, transcriptomic, proteomic, or interatomic cell measurements constitute fertile ground for a combination of computational machine learning with biomedical applications to analyze, diagnose and treat common and chronic ailments using cutting-edge technologies. We introduce and define the concepts and show potential application to some of the most commonly occurring human ailments; we also discuss options for structures and data integration strategies that are applied today in Machine Learning and Deep Neural Network Learning to diagnose and treat diseases and discuss sample scholarly work that shows the efficacy of this approach.
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El-Hassan, A. (2024). Machine Learning from Multi-omics: Applications and Data Integration. In: Alkhateeb, A., Rueda, L. (eds) Machine Learning Methods for Multi-Omics Data Integration. Springer, Cham. https://doi.org/10.1007/978-3-031-36502-7_2
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DOI: https://doi.org/10.1007/978-3-031-36502-7_2
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