報告題目： Integration of Brain Imaging and Genomic Data for Precision Medicine
In this talk, I will present our recent research work at Tulane Multiscale Bioimaging and Bioinformatics Laboratory (http://www.tulane.edu/~wyp/). First, I will give an overview of multiscale and multimodal imaging and genomics techniques, which are driving precision medicine. Then, I will show how we develop computational and statistical methods such as sparse models and deep learning for integrative analysis of multi-omics and imaging data. In particular, I will demonstrate their novel applications to the extraction of biomarkers for improved diagnosis of mental illnesses (e.g., schizophrenia). Finally, I will talk about our latest research efforts on the study of brain development of adolescents using fMRI, MEG and genomics data. Examples such as the prediction of IQ with brain network as fingerprints will be given.
Dr. Yu-Ping Wang received the BS degree in applied mathematics from Tianjin University, China, in 1990, and the MS degree in computational mathematics and the PhD degree in communications and electronic systems from Xi’an Jiaotong University, China, in 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. 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. He 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 and IEEE/ACM Trans. Computational Biology and Bioinformatics (TCBB). 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: http://www.tulane.edu/~wyp/
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