Multi-study inference of regulatory networks for more accurate models of gene regulation.

ABSTRACT: Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach

SUBMITTER: Castro DM 

PROVIDER: S-EPMC6363223 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature