Learning and Control Lecture 2022系列学术报告——

Inference of disease-associated microbial gene modules based on metagenomic and meta-transcriptomic data

——刘丙强 （山东大学金沙集团1862cc橙色）

报告人：刘丙强博士，山东大学金沙集团1862cc橙色

报告题目：Inference of disease-associated microbial gene modules based on metagenomic and meta-transcriptomic data

报告时间：2022年12月1日 星期四 8:30-10:30

报告地点：腾讯会议 ID: 328-757-3757

报告摘要： The identification of disease-associated microbial characteristics is crucial for disease diagnosis and therapy. However, the heterogeneity, high dimensionality, and large amounts of microbial data present tremendous challenges for the discovery of key microbial features. In this paper, we present IDAM, a novel computational method for disease-associated gene module inference from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operon) and regulatory mechanisms (gene co-expression patterns) to explore gene modules associated with specific phenotypes using a mathematical graph model, without relying on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome and demonstrated the superior performance of IDAM in disease-associated characteristics inference compared to popular tools. We also showed high reproducibility of the inferred gene modules of IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics, and potentially pave the way for understanding the role of the microbiome in human diseases.

报告人简介：刘丙强，山东大学金沙集团1862cc橙色教授、博士生导师、副院长，山东大学杰出中青年学者，山东省“泰山学者”青年专家。本硕博均毕业于山东大学金沙集团1862cc橙色。其间赴美国乔治亚大学联合培养。2010年留校任教。主要从事生物信息学研究，利用图论、组合最优化和机器学习的理论与方法来研究生物医学大数据处理与分析中面临的计算挑战问题。主持国家重点研发计划、基金委面上项目等科研项目。担任中国工业与应用数学学会数学生命科学专委会委员，中国运筹学会计算系统生物学分会理事，中国计算机学会生物信息学专委会委员，中国自动化学会智能健康与生物信息专委会委员，山东省生物信息学会（筹）副理事长等学术职务。