On fusion methods for knowledge discovery from multi-omics datasets

• Main Authors: Edwin Baldwin, Jiali Han, Wenting Luo, Jin Zhou, Lingling An, Jian Liu, Hao Helen Zhang, Haiquan Li
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: Computational and Structural Biotechnology Journal
• Subjects: Multi-omics; Data integration; Data fusion; Model fusion
• Online Access: http://www.sciencedirect.com/science/article/pii/S2001037019304155

Recent years have witnessed the tendency of measuring a biological sample on multiple omics scales for a comprehensive understanding of how biological activities on varying levels are perturbed by genetic variants, environments, and their interactions. This new trend raises substantial challenges to data integration and fusion, of which the latter is a specific type of integration that applies a uniform method in a scalable manner, to solve biological problems which the multi-omics measurements target. Fusion-based analysis has advanced rapidly in the past decade, thanks to application drivers and theoretical breakthroughs in mathematics, statistics, and computer science. We will briefly address these methods from methodological and mathematical perspectives and categorize them into three types of approaches: data fusion (a narrowed definition as compared to the general data fusion concept), model fusion, and mixed fusion. We will demonstrate at least one typical example in each specific category to exemplify the characteristics, principles, and applications of the methods in general, as well as discuss the gaps and potential issues for future studies.