ArticleScientific reports2019
Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.Computational and structural biotechnology journal · 2023Article
- FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.Frontiers in genetics · 2020Article
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5 authors.
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Abstract
High coverage and mutual exclusivity (HCME), which are considered two combinatorial properties of mutations in a collection of driver genes in cancers, have been used to develop mathematical programming models for distinguishing cancer driver gene sets. In this paper, we summarize a weak HCME pattern to justify the description of practical mutation datasets. We then present AWRMP, a method for identifying driver gene sets through the adaptive assignment of appropriate weights to gene candidates to tune the balance between coverage and mutual exclusivity. It embeds the genetic algorithm into the subsampling strategy to provide the optimization results robust against the uncertainty and noise in the data. Using biological datasets, we show that AWRMP can identify driver gene sets that satisfy the weak HCME pattern and outperform the state-of-arts methods in terms of robustness.
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