ArticleProceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms. SIAM Conference on Applied and Computational Discrete Algorithms (2021 : Online)2021
Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition.
Article in Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms. SIAM Conference on Applied and Computational Discrete Algorithms (2021 : Online), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition.Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms. SIAM Conference on Applied and Computational Discrete Algorithms (2021 : Online) · 2021Article
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5 authors.
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Abstract
We present a new combinatorial model for identifying regulatory modules in gene co-expression data using a decomposition into weighted cliques. To capture complex interaction effects, we generalize the previously-studied weighted edge clique partition problem. As a first step, we restrict ourselves to the noise-free setting, and show that the problem is fixed parameter tractable when parameterized by the number of modules (cliques). We present two new algorithms for finding these decompositions, using linear programming and integer partitioning to determine the clique weights. Further, we implement these algorithms in Python and test them on a biologically-inspired synthetic corpus generated using real-world data from transcription factors and a latent variable analysis of co-expression in varying cell types.
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