Integration of multi-omics and brain imaging data 多基因组学影像大数据的融合


Speaker:Yu-Ping Wang, PhD, Tulane University
Host:Xiahai Zhuang, School of Data Science, Fudan University
Time:10:00-11:00, Jan 7, 2020
Location:Zibin N102, Fudan University
Abstract:Recent years have witnessed the convergence of multiscale and multimodal brain imaging and omics techniques, showing great promise for systematic and precision medicine. In the meantime, they also bring about significant data analysis challenges for integrating and mining these large volumes of heterogeneous datasets. In this talk, firstly I will present our latest developments of machine learning and statistical models such as sparse regressions, distance correlation, deep collaborative learning for the representation and analysis of large-scale imaging multi-omics data. Secondly, I will present examples of applying these models to the extraction of biomarkers from (epi)genomics and MRI imaging data. Thirdly, I will focus on the integration of multiscale genomic and imaging data for improved diagnosis of mental illnesses (e.g., schizophrenia). Finally, I will show how to build brain networks and use them as fingerprints to recognize different age groups and predict IQs.

Dr. Yu-Ping Wang is currently a full Professor of Biomedical Engineering and Global Biostatistics & Data Sciences at Tulane University School of Science and Engineering & School of Public Health and Tropical Medicine. He is also a member of Tulane Center of Bioinformatics and Genomics, Tulane Cancer Center and Tulane Neuroscience Program. His research interests have been computer vision, signal processing and machine learning with applications to biomedical imaging and bioinformatics, where he has over 200 peer reviewed publications. He has been on numerous program committees and NSF and NIH review panels, and served as editors for several journals such as J. Neuroscience Methods, IEEE/ACM Trans. Computational Biology and Bioinformatics (TCBB) and IEEE Trans. Medical Imaging (TMI). His recent effort has been bridging the gap between biomedical imaging and genomics. For this work, he was elected to be a fellow of American Institute of Biological and Medical Engineering (AIMBE). More about his research can be found at his website: