ArticleBioinformatics advances2025
A fast method for extracting essential and synthetic lethality genes in GEM models.
Article in Bioinformatics advances, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Obtaining PDC and other high-added value products from lignin byJournal of integrative bioinformatics · 2025Article
- Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities.Scientific reports · 2024Article
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Authors and funding
2 authors.
Funding
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Abstract
Summary: Exploring and categorizing essential and synthetic lethality genes is crucial in developing effective and targeted therapies for various diseases. This endeavor hinges upon genetic minimal cut sets, which also find utility in metabolic engineering. Different methods have been suggested for calculating genetic minimal cut sets. Still, with the emergence of numerous new models and their increasing complexity, it has become essential to introduce new algorithms in this field. This paper presents a new algorithmic approach for computing genetic minimal cut sets, which utilizes linear programming techniques to improve temporal efficiency. The key concept of the method is to use a k-representative subset to replace the target set with a smaller, yet representative, one. We have analyzed its efficiency in terms of running times compared to gMCSPy, the most recent published research on computing genetic minimal cut sets. Availability and implementation: Software and additional material are freely available at https://github.com/biogacop/fastMethod.
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Registered trials
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