Bio: Dr. Mingliang Fang is currently working as a full professor at Fudan University, Shanghai, China. Prior to this, he was an Assistant Professor at Nanyang Technological University, Singapore. Dr. FANG received his Ph.D. degree in Environmental Chemistry and Toxicology from Duke University, United States in 2015. Then, he moved to The Scripps Research Institute (TSRI) as a Postdoctoral Research Associate in the field of metabolomics. Currently, his main research is on the development of human exposome methods, biotransformation of environmental chemicals, and the use of omics techniques to evaluate the toxicity of pollutants or their mixture at environmentally relevant levels. He is also quite interested in the application of artificial intelligence in the chemical identification using high-res mass spectrometry and toxicity prediction. Dr. Fang has published more than 130 peer-reviewed papers on the high-tier journals including Nature Nanotechnology, Nature Chemical Biology, PNAS, ES&T+ES&TL, Environmental Health Perspective, and Analytical Chemistry as the leading authors. He also works as Associate Editor of Environmental Pollution (Elsevier) and Editorial Board members in Exposome (Oxford), ACS ES&T Letter, ACS ES&T Water, Environmental Science: Processes & Impacts (RSC), Environment International, Environmental Research, and Chemosphere. He is also the recipient of 2023 Chemical Research in Toxicology Young Investigator Award of ACS and the Reviewer Award for ES&T (2022 and 2023).

Abstract: Exposome has become the hotspot of next-generation health studies. To date, there is no available effective platform to standardize the analysis of exposomic data. In this presentation, we aim to propose one new framework of exposomic analysis and build up one integrated platform “ExposomeX” to expediate the discovery of the “Exposure-Biology-Disease” nexus. We have developed several standardized modules to accomplish six major functions including statistical analysis, exposome database search, mass spectrometry data processing, meta-analysis, biological link via pathway integration and protein-protein interaction and data visualization. Using ExposomeX, we can effectively analyze the multiple-dimensional exposomic data and investigate the “Exposure-Biology-Disease” nexus by exploring mediation and interaction effects, understanding statistical and biological mechanisms, strengthening prediction performance, and automatically conducting meta-analysis based on well-established literature databases. ExposomeX can efficiently help discover new associations, as well as relevant in-depth biological pathways via protein-protein interaction and gene ontology network analysis (http://www.exposomex.cn/).