A review on machine learning principles for multi-view biological data integration

• Published in: Briefings in bioinformatics Vol. 19; no. 2; pp. 325 - 340
• Main Authors: Li, Yifeng, Wu, Fang-Xiang, Ngom, Alioune
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.03.2018; Oxford Publishing Limited (England)
• Subjects: Animals; Artificial intelligence; Artificial neural networks; Bayesian analysis; Bayesian theory; clinical trials; Clustering; Data integration; Decision making; deep learning; Gene Regulatory Networks; genomics; high-throughput nucleotide sequencing; Humans; Integration; Learning algorithms; Machine Learning; Mathematical models; matrix factorization; Models, Biological; multi-omics data; multiple kernel learning; network fusion; Neural networks; Next-generation sequencing; prediction; Prediction models; random forest; Similarity; Systems Biology - methods; multi-omics data; network fusion; matrix factorization; deep learning; multiple kernel learning; random forest; data integration
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbw113

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.
NRC publication: Yes