Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis
biological data analysis, biological network, data mining and knowledge discovery, deep learning, link prediction, machine learning, relation inference
Frontiers in Genetics
Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.
Original Publication Date
DOI of published version
UNI ScholarWorks, Rod Library, University of Northern Iowa
Lee, Bohyun; Zhang, Shuo; Poleksic, Aleksandar; and Xie, Lei, "Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis" (2020). Faculty Publications. 357.