Work

A new evolutive clustering algorithm (eSIMBA) for active module identification in p-value attributed temporal biological networks

Year: 2026

Type: article

Abstract: Biological processes are inherently dynamic: gene expression and molecular interactions fluctuate as cellular mechanisms evolve over time. Active Module Identification Methods (AMIMs) capture these functional behaviours by identifying sets of molecular entities exhibiting coordinated activity. Yet, understanding biological systems requires more than a static picture and calls for the temporal contextualization of such modular dynamics. To address this need, the proposed algorithm combines static and sliding-time-window community detection using a greedy matching procedure that maintains continuity across time. This method detects active modules at each time point and consistently tracks their evolution. Extensive experiments performed on both synthetic and real-world biological networks confirm its effectiveness. By explicitly embracing temporal variability, the proposed approach reveals how active modules emerge, split, merge, or disappear, providing new insights into the dynamic organization of node-attributed biological networks.

Source: Scientific Reports

DOI: 10.1038/s41598-026-70249-3

Authors: Nina Singlan, Fadi Abou Choucha, Claude Pasquier

Institutions: Centre National de la Recherche Scientifique, Université Côte d'Azur, Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: Agence Nationale de la Recherche