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Machine Learning Methods for Multi-Omics Data Integration by Abedalrhman Alkhateeb (editor), Luis Rueda (editor) is a nonfiction available to read on EtoBox.

What is Machine Learning Methods for Multi-Omics Data Integration about?

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 omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for val

Who reads Machine Learning Methods for Multi-Omics Data Integration?

Author

Abedalrhman Alkhateeb (editor), Luis Rueda (editor)

Publisher

Springer International Publishing AG

Published

2023

Language

EN

ISBN

9783031365027

Category

nonfiction

Subjects

Science, Biology, Computer Science