Topology-based metrics for finding the optimal sparsity in gene regulatory network inference

Authors

Mateusz Garbulowski (ORCID: https://orcid.org/0000-0002-2497-194X)
Thomas Hillerton (ORCID: https://orcid.org/0000-0002-6362-0659)
Erik L. L. Sonnhammer (ORCID: https://orcid.org/0000-0002-9015-5588)
N. Lundqvist

Institutions

Stockholm University (SE)
Science for Life Laboratory (SE)

Publication Details

Journal: Bioinformatics
Published: 2025-03-24
DOI: https://doi.org/10.1093/bioinformatics/btaf120
Primary Topic: Gene Regulatory Network Analysis
Type: article
Field-Weighted Citation Impact: 0.00

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article

Topology-based metrics for finding the optimal sparsity in gene regulatory network inference

Mateusz Garbulowski, Thomas Hillerton, Erik L. L. Sonnhammer, N. Lundqvist

Mar 24, 2025

Bioinformatics

Gene Regulatory Network Analysis

article

Topology-based metrics for finding the optimal sparsity in gene regulatory network inference

Mateusz Garbulowski, Thomas Hillerton, Erik L. L. Sonnhammer, N. Lundqvist

March 24, 2025article en

Abstract

No abstract available for this paper.

BioinformaticsVol. 41(5)

Stockholm University (SE), Science for Life Laboratory (SE)

Peace, Justice and strong institutions

Openalex Percentile: Top 95%

Gene Regulatory Network Analysis

DOI Open Access PDF OpenAlex

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Topology-based metrics for finding the optimal sparsity in gene regulatory network inference — Mateusz Garbulowski, Thomas Hillerton, et al. · Bioinformatics (2025) | TGRS Research Map | TGRS