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

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 d...