Abstract
Identifying significant mutations in cancer is a challenging problem in Cancer Genomics. Computational methods for identifying significant mutations have been developed in recent years. In this work, we present a flexible computational method named GeNWeMME (Gene Network + Weighted Mutations + Mutual Exclusivity). Our method uses an extensive biological base for prioritizing groups of significant and related genes in cancer. Our method considers data about mutations, type of mutations, gene interaction networks and mutual exclusivity pattern. All these aspects can be used according to the objective of the analysis by cancer genomics professionals, that can choose weights for each aspect. We test our method in four types of cancer where it was possible to identify known cancer genes and suggest others for further biological validation.
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Cutigi, J.F., Evangelista, A.F., Simao, A. (2020). GeNWeMME: A Network-Based Computational Method for Prioritizing Groups of Significant Related Genes in Cancer. In: Kowada, L., de Oliveira, D. (eds) Advances in Bioinformatics and Computational Biology. BSB 2019. Lecture Notes in Computer Science(), vol 11347. Springer, Cham. https://doi.org/10.1007/978-3-030-46417-2_3
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DOI: https://doi.org/10.1007/978-3-030-46417-2_3
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