SPS BSI Webinar: Integration of Brain Imaging and Genomics with Interpretable Multimodal Collaborative Learning
Date: 26 September 2025
Time: 1:00 PM ET (New York Time)
Presenter: Dr. Yu-Ping Wang
Meeting information:
Meeting number: 2530 653 2710
Password: EgZjhhED686 (34954433 when dialing from a phone or video system)
Join by phone:
+1-415-655-0002 US Toll
Access code: 2530 653 2710
https://gsumeetings.webex.com/gsumeetings/j.php?MTID=m6a1bee0eb7a11df1f34abd8b884f2c8e
Join us Friday, September 26th, 2025, at 1:00 PM ET for an exciting virtual talk by Dr. Yu-Ping Wang entitled: “Integration of Brain Imaging and Genomics with Interpretable Multimodal Collaborative Learning” as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society.
Abstract
Integration of Brain Imaging and Genomics with Interpretable Multimodal Collaborative Learning
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 bring significant data analysis challenges when integrating and mining these large volumes of heterogeneous datasets. In this work, we first introduce a linear collaborative learning model to combine both regression and correlation analysis such as CCA. To further capture complex interactions both within and across modalities, we develop an interpretable multimodal deep learning-based integration model to perform heterogeneous data integration and result interpretation simultaneously. The proposed model can generate interpretable activation maps to quantify the contribution of imaging or omics features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers. Finally, we apply and validate the model in the study of brain development with integrative analysis of multi-modal brain imaging and genomics data. We demonstrate its successful application to both the classification of cognitive function groups and the discovery of underlying genetic mechanisms.
Biography
Yu-Ping Wang received the B.S. degree in applied mathematics from Tianjin University, China, the M.S. degree in computational mathematics and the Ph.D. degree in communications and electronic systems from Xi’an Jiaotong University, China, in 1990, 1993 and 1996, respectively. After his graduation, he had visiting positions at the Center for Wavelets, Approximation and Information Processing of the National University of Singapore and Washington University Medical School in St. Louis.
He's currently a Professor of Biomedical Engineering, Computer Sciences, Neurosciences, and Biostatistics & Data Sciences at Tulane University. From 2000 to 2003, he worked as a senior research engineer at Perceptive Scientific Instruments, Inc., and then Advanced Digital Imaging Research, LLC, Houston, Texas. In the fall of 2003, he returned to academia as an assistant professor of computer science and electrical engineering at the University of Missouri-Kansas City.
Dr. Wang’s recent effort has been bridging the gap between biomedical imaging and genomics, where has over 250 journal publications. Dr. Wang is a fellow of AIMBE and won the 2022 Tulane Convergence Award for his effort in bridging gaps between science, engineering and biomedicine. He has served for numerous program committees and NSF and NIH review panels and is currently an associate editor for J. Neuroscience Methods, IEEE/ACM Trans. Computational Biology and Bioinformatics (TCBB) and IEEE Trans. Medical Imaging (TMI). More about his research can be found at his lab website: http://www.tulane.edu/~wyp/
Recommended Articles:
• Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Gang Qu, Biao Cai, Gemeng Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang, Interpretable multimodal fusion networks reveal mechanisms of brain cognition, IEEE Transactions on Medical Imaging, Page(s):1-1, Date of Publication: February 08 2021 (Link to Paper).
• Wenxing Hu, Biao Cai, Aiying Zhang, Vince D. Calhoun, Yu-Ping Wang, Deep collaborative learning with application to multimodal brain development study, IEEE Transactions on Biomedical Engineering, Date of Publication: 13 March 2019. (Link to Paper).
• Pascal Zille, Vince D. Calhoun, Yu-Ping Wang, Enforcing Co-expression Within a Brain-Imaging Genomics Regression Framework, IEEE Transactions on Medical Imaging, 28 June 2017, Page(s): 1-1. (Link to Paper).
