ArticleLife science alliance2023
A network embedding approach to identify active modules in biological interaction networks.
Article in Life science alliance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Evaluation and Aggregation of Active Module Identification Algorithms.bioRxiv : the preprint server for biology · 2025Article
- A new Similarity Based Adapted Louvain Algorithm (SIMBA) for active module identification in p-value attributed biological networks.Scientific reports · 2025Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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Abstract
The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine.
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