Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

Abstract

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-ofthe-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.

Data-Driven Modeling of Pregnancy-Related Complications

“…Another problem is that omics of high dimensionality but with low information content may preclude inclusion in the model of information from smaller, more dense omics. To address these issues, a wide variety of methods for multiomics integration is currently being developed in the field of bioinformatics [117][118][119][120], and there are community-driven efforts to maintain an overview of relevant work and software packages i . This work incorporates approaches from diverse fields of machine learning including Bayesian concepts [121,122], network analysis [117,118], and deep-learning techniques [123][124][125].…”

“…To address these issues, a wide variety of methods for multiomics integration is currently being developed in the field of bioinformatics [117][118][119][120], and there are community-driven efforts to maintain an overview of relevant work and software packages i . This work incorporates approaches from diverse fields of machine learning including Bayesian concepts [121,122], network analysis [117,118], and deep-learning techniques [123][124][125]. Similar to the single-omics case, methods for predictive studies often incorporate dimensionality reduction and data integration using a mixture of variable selection (e.g., [120]) and representation learning (e.g., [126]) to reduce the number of features and to calibrate their influence on the model between omics.…”

Interpretation of network-based integration from multi-omics longitudinal data

“…Multi-omics network integration in literature Multi-omics networks are the key to illustrate topological relationships between different molecular species [23]. The HetioNet network is currently one of the most impressive multi-omics networks.…”

Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

“…Multi-layered networks for example have been proposed as a powerful tool used to establish the necessary connection between different types of information: it does provide a natural way to represent the structure of a biological system, and the relationships between different layers in the network may represent effects which cannot be described just by statistical correlations (as it happens in genome-wide association studies, GWAS) ( Lee et al, 2019 ). Network-based methods appear also a very appropriate direction to combine data integration tools with a holistic interpretation of phenotypes and their determinants.…”