Titlebook: Machine Learning Methods for Multi-Omics Data Integration; Abedalrhman Alkhateeb,Luis Rueda Book 2024 The Editor(s) (if applicable) and Th

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978-3-031-36504-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Machine Learning from Multi-omics: Applications and Data Integration,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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en databases and repositories facilitate replication.Special.The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple

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Machine Learning Approaches for Multi-omics Data Integration in Medicine,search for disease diagnosis, monitoring, and treatment options is how to integrate high-dimensional data from omics. This chapter focused on machine learning methods for multi-omics data integration.

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Introduction to Multiomics Technology,ing researchers to gain a deeper understanding of its complexity and heterogeneity. The integration of multi-omics data can reveal novel biological pathways, biomarkers, and potential therapeutic targets. In this introduction to multiomics chapter, we will discuss several omics types and their commonly used techniques.