MCWCM: Multi-Criteria Ranking and Weighted Control Model for Identifying Key Drivers in cancer
Authors: 
Bolin Chen, Zhengyu Wang, Ziyuan LiAuthors Info & Claims
Bolin Chen, Zhengyu Wang, Ziyuan LiAuthors Info & ClaimsArticle No.: 60, Pages 1 - 6
Abstract
A key task in cancer genomics research is the identification of cancer driver genes. In recent years, control methods have been applied to identify cancer drivers with notable success. However, these methods primarily focus on exploring the biological network topology, potentially overlooking the critical influence of node weights derived from genomic data. Moreover, many methods have overly simplistic evaluation metrics. In this work, we propose a novel control theory-based method, along with a comprehensive evaluation metric, for detecting both coding and non-coding drivers. This method comprises three main procedures: First, a fusion network of mutation messages is constructed by integrating gene networks, expression data and mutation data. Second, applying a weighted control method to identify candidate drivers. Third, a comprehensive evaluation metric based on multiple criteria is computed to rank the candidate drivers. The application of MCWCM to the BRCA dataset demonstrates its enhanced effectiveness over current leading methods in identifying coding cancer drivers.
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Index Terms
- MCWCM: Multi-Criteria Ranking and Weighted Control Model for Identifying Key Drivers in cancer
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November 2024
614 pages
ISBN:9798400713026
DOI:10.1145/3698587
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Published: 16 December 2024
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