Construção e Avaliação de Modelos de Aprendizado de Maquina para a Classificação de Mutações Missenses Associadas ao Câncer: ˆ Abordagem Utilizando Redes de Interação de Resíduos (RING)

Abstract

The early diagnosis plays a crucial role in the effective treatment of cancer, driving the quest for enhanced methods of identifying genetic mutations associated with this pathology. This study focuses on the analysis of Residue Interaction Networks (RINGs), an alternative approach for visualizing proteins, as a promising source of supplementary information for the detection of missense mutations linked to various cancer variations. Machine learning techni ques will be employed to assess the potential of RINGs in predicting these mutations. It has been observed that such information is vital for identifying genuinely harmful mutations in individualized machine learning models.