讲座题目：Inferring disease-associated microRNAs using semi-supervised multi-label graph convolutional networks
报告摘要：MicroRNAs (miRNAs) play crucial roles in many biological processes involved in diseases. The associations between diseases and protein coding genes (PCGs) have been well investigated, and further the miRNAs in-teract with PCGs to trigger them to be functional. Thus, it is imperative to computationally infer disease-miRNA associations under the context of interaction networks. In this study, we present a computational method, DimiG, to infer miRNA-associated diseases using semi-supervised Graph Convolutional Network model (GCN). DimiG is a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations and tissue expression profiles. DimiG is trained on disease-PCG associations and a graph constructed from interaction networks of PCG-PCG and miRNA-PCG using semi-supervised GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set collected from verified disease-miRNA associations. Our results demonstrate that the new DimiG yields promising performance and outperforms the best published baseline method not trained on disease-miRNA associations by 11% and is also comparable to two state-of-the-art supervised methods trained on disease-miRNA associations. Three case studies of prostate cancer, lung cancer and Inflammatory bowel disease further demonstrate the efficacy of DimiG, where the top miR-NAs predicted by DimiG for them are supported by literature or databases.
报告人简介：潘小勇，比利时根特大学助理研究员，荷兰鹿特丹大学医疗信息学部博士后，硕士毕业于上海交通大学、博士毕业于丹麦哥本哈根大学。研究方向主要包括：电子病历数据分析、生物信息学、机器学习、文本挖掘，在应用机器学习解决生物医学等领域的问题有丰富的科研经验。Bioinformatics，Computational biology and Chemistry, Journal of Biomedical and Health Informatics, Interdisciplinary Sciences: Computational Life Sciences等期刊审稿人，已在国际知名期刊发表SCI检索论文30余篇。