From complex data to biological insight: 'DEKER' feature selection and network inference.

ABSTRACT: Network inference is a valuable approach for gaining mechanistic insight from high-dimensional biological data. Existing methods for network inference focus on ranking all possible relations (edges) among all measured quantities such as genes, proteins, metabolites (features) observed, which yields a dense network that is challenging to interpret. Identifying a sparse, interpretable network using these methods thus requires an error-prone thresholding step which compromises their performance. In this article we propose a new method, DEKER-NET, that addresses this limitation by directly identifying a sparse, interpretable network without thresholding, improving real-world performance. DEKER-NET uses a novel machine learning method for feature selection in an iterative framework for network

SUBMITTER: Hayes SMS 

PROVIDER: S-EPMC8837529 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature