Artificial Neural Networks

Airey, Edward, Portelli, Stephanie, Xavier, Joicymara S, Myung, Yoo Chan, Silk, Michael, Karmakar, Malancha, Velloso, João P L, Rodrigues, Carlos H M, Parate, Hardik H, Garg, Anjali, Al-Jarf, Raghad, Barr, Lucy, Geraldo, Juliana A, Rezende, Pâmela M, Pires, Douglas E V, Ascher, David B, Xavier, Joicymara S., Velloso, João P. L., Rodrigues, Carlos H. M., Parate, Hardik H., Geraldo, Juliana A., Rezende, Pâmela M., Pires, Douglas E. V., Ascher, David B., Edward Airey, Stephanie Portelli, Joicymara S. Xavier, Yoo Chan Myung, Michael Silk, Malancha Karmakar, João P. L. Velloso, Carlos H. M. Rodrigues, Hardik H. Parate, Anjali Garg, Raghad Al-Jarf, Lucy Barr, Juliana A. Geraldo, Pâmela M. Rezende, Douglas E. V. Pires, David B. Ascher

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.