Machine Learning Methods for Multi-Omics Data Integration

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.