報告人：宋江寧

澳大利亞蒙納士大學生物醫學發現和蒙納士數據期貨研究所副教授、研究組長

報告時間：5月17日（周三）9:30-11:00

報告地點👳🏻‍♂️👮🏻：生物藥學樓2-116會議室

報告人簡介：

Dr Song is an Associate Professor and group leader in the Monash Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Australia. Trained as a bioinformatician and data-savvy scientist, he has a strong specialty in Artificial Intelligence, Bioinformatics, Comparative Genomics, Cancer Genomics, Computational Biomedicine, Data Mining, Infection and Immunity, Machine Learning, Proteomics, and 'Biomedical Big Data', which are sought-after expertise and skill sets in the data-driven biomedical sciences. He is a member of the Monash Centre for Data Science and also Associate Investigator of the ARC Centre of Excellence in Advanced Molecular Imaging at Monash University. He is an Associate Editor of BMC Bioinformatics, Frontiers in Bioinformatics, Genomics, Proteomics and Bioinformatics, an Editorial board member of BMC Genomic Data, Biomolecules and an Advisory Board member of Current Protein & Peptide Science. He is the founding member of Monash University's Centre to Impact AMR and is responsible for developing the Centre's AMR Big Data and AI-Driven research capacity. His data-driven bioinformatics research has been well funded by the Australian MRFF, NHMRC, ARC and Monash Major Inter-Disciplinary research funding schemes.

報告摘要：

The rapid accumulation of molecular data motivates the 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 software tools can generate, analyze and visualize various representations of biological sequence, structure, and ligand data. With the assistance of the tools, users can encode their molecular

data into representations that facilitate the construction of predictive models and analytical studies. In the talk, I will also combine our recent research works to illustrate how such AI tools can be leveraged to accelerate and paradigm-shift the data-driven research in bioinformatics, computational biology, and biomedicine.