TY - JOUR
T1 - On fusion methods for knowledge discovery from multi-omics datasets
AU - Baldwin, Edwin
AU - Han, Jiali
AU - Luo, Wenting
AU - Zhou, Jin
AU - An, Lingling
AU - Liu, Jian
AU - Zhang, Hao Helen
AU - Li, Haiquan
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Data fusion
KW - Data integration
KW - Model fusion
KW - Multi-omics
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U2 - 10.1016/j.csbj.2020.02.011
DO - 10.1016/j.csbj.2020.02.011
M3 - Review article
AN - SCOPUS:85081650557
SN - 2001-0370
VL - 18
SP - 509
EP - 517
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -