Aplicação de simulated annealing para descobrir mutações drivers

• Paulo Henrique Ribeiro IFSP
• Jorge Francisco Cutigi IFSP
• Adriane Feijó Evangelista Barretos Cancer Hospital
• Adenilso da Silva Simão USP

DOI: https://doi.org/10.5753/sbcas.2022.222451

Abstract

Cells with driver mutations can proliferate faster than other cells, generating more daughter cells and causing the appearance of the tumor. In this context, there are computational methods for identifying possible driver mutations in a sample of cells. DriverNet is a method for classifying mutations in a sample, indicating those most likely to be driver mutations. This work presents a new proposal to identify driver mutations based on the DriverNet method. The new proposal, which requires a smaller set of input data than the DriverNet method, identified genes related to cancer from datasets from the TCGA project.

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