报告摘要：Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and biological networks rely on association studies or observational causal-analysis approaches. This study introduces a novel approach that combines intervention operations and diffusion models within do-calculus framework by deep learning, i.e., Causal Diffusion Do-calculus (CDD) analysis, to infer causal networks between molecules. CDD is able to extract causal relations from observed data owing to its intervention operations, thereby significantly enhancing accuracy and generalizability of causal network inference. Computationally, CDD has been applied to both simulated data and real omics data, which demonstrates that CDD outperforms existing methods in accurately inferring gene regulatory networks and identifying causal links from genes to disease phenotypes. Especially, comparing with the Mendelian randomization algorithm and other existing methods, the CDD can reliably identify the disease genes or molecules for complex diseases with better performances. In addition, we also conducted the causal analysis between various diseases and the potential factors in different populations from UK Biobank database, which further validated the effectiveness of CDD.

报告人简介：刘小平，国科大杭高院生命与健康科学学院教授，2012年毕业于上海大学信息学与系统生物学专业，日本东京大学博士后，2017-2020年山东大学数学与统计学院研究员。主要研究领域是基于网络的复杂疾病机制分析、生物大数据的分析和处理、生物信息学和计算系统生物学等。在National Science Review、Nucleic Acids Research、Briefings in Bioinformatics、Cancer Letters、Bioinformatics、PLoS Computational Biology等杂志发表SCI论文40余篇。现为中国医药生物技术协会基因检测分会委员，中国运筹学会计算系统生物学分会会员，生化细胞协会分子系统生物学专委会委员。SCI杂志Mathematics编辑, SCI杂志Frontiers in Genetics和Symmetry-Basel的客座编辑。

报告时间：2024年11月1日 上午8:30-12:00

报告地点：北横楼1421