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Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference
Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Solna, Sweden..
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neurooncology and neurodegeneration. Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden..ORCID iD: 0000-0003-1758-1262
Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Solna, Sweden..
2022 (English)In: Frontiers in Genetics, E-ISSN 1664-8021, Vol. 13, article id 855770Article in journal (Refereed) Published
Abstract [en]

Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, which is guessed to be reasonable. However, this does not guarantee finding the GRN that has the correct sparsity or is the most accurate one. In this study, we provide a general approach for identifying the most accurate and sparsity-wise relevant GRN within the entire space of possible GRNs. The algorithm, called SPA, applies a "GRN information criterion " (GRNIC) that is inspired by two commonly used model selection criteria, Akaike and Bayesian Information Criterion (AIC and BIC) but adapted to GRN inference. The results show that the approach can, in most cases, find the GRN whose sparsity is close to the true sparsity and close to as accurate as possible with the given GRN inference method and data.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA Frontiers Media S.A., 2022. Vol. 13, article id 855770
Keywords [en]
sparsity selection, information criteria, gene regulatory network inference, gene expression data, noise in gene expression
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Telecommunications
Identifiers
URN: urn:nbn:se:uu:diva-492108DOI: 10.3389/fgene.2022.855770ISI: 000892435200001PubMedID: 35923701OAI: oai:DiVA.org:uu-492108DiVA, id: diva2:1732617
Funder
Swedish Foundation for Strategic ResearchAvailable from: 2023-01-31 Created: 2023-01-31 Last updated: 2024-01-15Bibliographically approved

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Nelander, Sven

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