Pharma Focus Asia
KP - Sustainable Packaging Solutions

Machine Learning Methods for Multi-Omics Data Integration

Machine Learning Methods for Multi-Omics Data Integration

Pages: 168

Publisher: Springer Cham

Year of Publication: 2024

Author(s) : Abedalrhman Alkhateeb, Luis Rueda

Book Description:

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 validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data.

Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
 

https://link.springer.com/book/10.1007/978-3-031-36502-7

Latest Issue
Get instant
access to our latest e-book
Standard BioTools™MFA + MMA 2024Gibco Adherent Kidney Media PanelTFS - ODW - The Technology Debate: Single-Use vs. Stainless SteelAsia Healthcare Week 2024CPHI Korea 2024CHEMICAL INDONESIA 2024Thermofisher - Antibody Therapeutic PolishingINALAB 20244th Middle East Pharmacoeconomics and Drug Policy Summit 2024Digital Health Asia 2024Rehab Expo 2024ISPE Singapore Affiliate Conference & Exhibition 20242024 PDA Pharmaceutical Manufacturing & Quality Conference2024 PDA Cell and Gene Pharmaceutical Products Conference 2024 PDA Aseptic Manufacturing Excellence Conference2024 PDA Aseptic Processing of Biopharmaceuticals Conference
Searching for an end-to-end patient tech solution?