Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. Identifying noncoding risk variants remains a challenging task. Gao, Long Uzun, Yasin Gao, Peng He, Bing Ma, Xiaoke Wang, Jiahui Han, Shizhong Tan, Kai Identifying noncoding risk variants using disease-relevant gene regulatory networks.
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