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学术报告—Artificial intelligence to explore multi-modality molecular data and accelerate biomedical knowledge discovery

作者:于彬 审核人:杜军威 编辑:王华东 发布日期:2024-01-02

题目:Artificial intelligence to explore multi-modality molecular data and accelerate biomedical knowledge discovery

报告人:Prof. Jiangning Song

Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology

Faculty of Medicine, Nursing and Health Sciences

Monash University, Australia

时间:2024年1月5日  上午10:30

地点:学术报告厅—图书馆楼4008

摘要:

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. We address this vital need by developing holistic software platforms that can generate features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use AI platforms can generate, analyse and visualize more than 180 representations for biological sequences, structures and ligands. With the assistance of the AI tools, users can encode their molecular data into representations that greatly facilitate construction of predictive models and analytical studies. In my talk, I will also illustrate how such AI tools can be leveraged to accelerate and paradigm-shifting research in bioinformatics, computational biology, and biomedicine.

简介:

Dr. Song is founder and director of the AI-driven Bioinformatics & Biomedicine Group in the Monash Biomedicine Discovery Institute (BDI), Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. He is a member of the Monash Data Futures Institute, an Associate Investigator of the Australian Research Council (ARC) Centre of Excellence in Advanced Molecular Imaging, a founding member of the Centre to Impact on Antimicrobial Resistance, and a member of the Alliance for Digital Health (ADAM) at Monash University. His lab is interested in tackling biomedical problems with large amounts of data generated by high-throughput techniques that span two strategical research programs, involving both Cancer and Infection and Immunity Programs.

Dr. Song is an Associate Editor or Editorial Board Member of 10 international journals, includingJournal of Biomedical and Health Informatics,BMC Bioinformatics,Genomics, Proteomics & Bioinformatics,Frontiers in Bioinformatics,BMC Genomic Data,Computers in Biology and Medicine,Biomolecules, and Advisory Board Member ofCurrent Protein & Peptide Science.In the past five years since 2018, he has published 161 peer-reviewed journal papers in top-tier journals, e.g.Nature Methods,Nature Machine Intelligence,Science Immunology,Science Advances,Nature Communications,Lancet Planetary Health,Nucleic Acids Research,Briefings in Bioinformatics,Cell Reports,PLoS Biology,Genomics Proteomics & BioinformaticsandBioinformatics.

Dr. Song is a PC member for more than 20 international conferences on bioinformatics, computational biology, health informatics, and e-Science, including BIBM, IEEE e-Science, InCoB, ISB, ICPB, ICIC, IIBM, and GIW etc. He is an invited reviewer for >60 journals in bioinformatics, computational biology, machine learning, data mining, systems biology, and chemoinformatics. As Chief Investigator, he has been awarded ~40 grants (~$22.0M, 25 as CIA/CIB) by the US National Institutes of Health (NIH), Australian Medical Research Future Fund (MRFF), NHMRC and ARC, and other granting bodies. He has supervised 10 PhD students to successful completion. Some of his PhD graduates have proceeded with the career path of working as academics in universities and research institutions or in the industry.