Title: HOGMMNC: A higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification

Abstract：The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene-drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug action, but is also important in the development of effective treatments for patients. However, accurately determining the complex associations among pharmacogenomic data remains challenging. We propose a higher order graph matching with multiple network constraints (HOGMMNC) model to accurately identify gene-drug modules. The HOGMMNC model aims to capture the inherent structural relations within data drawn from multiple sources by hypergraph matching. The proposed technique seamlessly integrates prior constraints to enhance the accuracy and reliability of the identified relations. The modules identified by HOGMMNC provide new insights into the molecular mechanisms of drug action and provide patients with more effective treatments. Our proposed method can be applied to the study of other biological correlated module identification problems (e.g., miRNA-gene, genemethylation, and gene-disease).

报告人简介： 蔡宏民，华南理工大学计算机学科学与技术学院教授，博士生导师，2014广东省优秀青年教师， 2016年科技部重点领域创新团队核心成员，2016广东省计算智能与网络空间信息重点实验室核心成员，全国系统生物学专业委员会委员，生物信息学与人工生命专业委员会委员，CCF生物信息学专业组委员会委员。2012年9月至今在华南理工大学任教，2016年9月破格晋升博士生导师，同年破格晋升教授。研究兴趣包括医学图像分析与理解和多源生物数据信息分析。作为访问研究人员在哈佛大学Center for Bioinformatics 实验室、宾夕法尼亚大学（UPenn） Section for Biomedical Analysis 实验室从事生物医学图像方面的研究。受邀访问香港浸会大学、日本京都大学等从事生物信息方面的合作研究。在国际顶级杂志及一流会议上发表论文40多篇。主持或完成国家自然科学基金三项，省部级项目十多项，累计获资助经费500+万元。