SscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.

ABSTRACT: Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally

SUBMITTER: Li H 

PROVIDER: S-EPMC10876991 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature