sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases - Li, Haibo; Yu, Zhenhua; Du, Fang; Song, Lijuan; Gao, Yang; Shi, Fangyuan (Frontiers in Immunology, 2024)
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, for predicting functional regulatory variants in autoimmune diseases and analyzing 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 curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-ofthe-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead SNPs and the proxy variants in autoimmune GWAS data.