Prof. Hongmin Cai
South China University of Technology, China
Dr. Hongmin Cai is a professor and doctoral supervisor at the School of Computer Science and Engineering, South China University of Technology. Prof. Cai is a guest professor at Kyoto University, a member of the Bioinformatics and Artificial Life Committee of CAAI, a member of the Bioinformatics Committee and Standing Committee of CCF, and a PC Member of many international conferences such as ISBI, ISBRA, ICIC, BIBM and GIW. Professor Cai is also the Chairman of ICDKE 2012, ICBBB2021, ICBBB 2022, Vice Chairman of Guangdong Translational Medicine Ophthalmology Branch, Vice Chairman of Guangdong Precision Medicine Application Society - Digital Intelligence Branch, Vice Chairman of Guangdong Biomedical Engineering Society Intelligent Medical Imaging Branch. Professor Cai has been involved in the research works on biomedical image and bioinformatics. He has accumulated rich research experience in the fields of medical image analysis and understanding, bioinformatics, multi-source data fusion, pattern recognition and data mining. In esteemed journals such as IEEE T-PAMI, IEEE T-Cybern, IEEE T-Image Proce., IEEE T-Medical Imaging, NeuroImage, Bioinformatics, Briefings in Bio., more than 100 SCI/EI papers have been published, including 80 SCI/EI papers regarding Professor Cai as the corresponding author or the first author. The total IF of the papers in recent 5 years exceeds 200.
Speech Title: "Recent Advances Integration Analysis for Biomedical Data from Multiple Sources"
With the popularization of new generation sequencing technology and the rapid development of single cell sequencing technology, multi-omics data at different scales are derived. With the rapid development of various new imaging technologies, multimodal image data under different imaging conditions are derived. Such multi-source data has problems such as small sample size, high dimension, multi-mode, cross-scale and multi-attribute missing, etc. It is of great scientific significance to integrate and analyze such multi-source heterogeneous data, which can provide computing tools for life computation and medical assisted diagnosis. Our lab focus on the integration theory on multi-source heterogeneous data. We established a tensor spectral clustering theory for small sample learning, to realize the effective fusion of multi-source information. We carried out on analyzing the data from micro multi-omics and macro medical imaging. This report will report the recent advances of our laboratory in the above aspects.
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