利用机器学习技术来加速生物医学知识发现
发布时间 :2023-05-12  阅读次数 :15804

报告人:宋江宁

澳大利亚蒙纳士大学生物医学发现和蒙纳士数据期货研究所副教授、研究组长

报告时间:5月17日(周三)9:30-11:00

报告地点:生物药学楼2-116会议室

 

报告人简介:

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.

 

报告摘要:

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.