biostudies-literatureUnknown49(1)Hayes SMSNetwork 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 Journal of pharmacokinetics and pharmacodynamics81-99https://www.ebi.ac.uk/biostudies/studies/S-EPMC8837529biostudies-literatureFrom complex data to biological insight: 'DEKER' feature selection and network inference.PMC8837529Sachs JRCho CRHayes SMSfalseFrom complex data to biological insight: 'DEKER' feature selection and network inference.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 2022-01-01T00:00:00Z2022 Feb2025-04-04T08:58:30.235Z2025-04-04T08:58:30.235ZS-EPMC88375293479157710.1007/s10928-021-09792-7