Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures.

ABSTRACT: With the surge of the high-throughput sequencing technologies, many genetic variants have been identified in the past decade. The vast majority of these variants are defined as variants of uncertain significance (VUS), as their significance to the function or health of an organism is not known. It is urgently needed to develop intelligent models for the clinical interpretation of VUS. State-of-the-art artificial intelligence (AI)-based variant effect predictors only learn features from primary amino acid sequences, leaving out information about the most important three-dimensional structure that is more related to its function. Methods: We proposed a deep convolutional neural network model named variant effect recognition network for BRCA1 (vERnet-B) to recognize the clinical pathog

SUBMITTER: Li C 

PROVIDER: S-EPMC9800725 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature