TALK TITLE: Artificial intelligence to explore multi-modality biomedical data and accelerate knowledge discovery
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 Al platforms can generate, analyze and visualize various representations of biological sequences, structures, and ligands.
With the assistance of Al tools, users can encode their molecular data into representations that facilitate the construction of predictive models and analytical studies.
In my talk, I will also illustrate how such Al tools can be leveraged to accelerate and paradigm-shift the data-driven research in bioinformatics ,computational biology, and biomedicine.
Jiangning is an Associate Professor and Group Leader in the Cancer and Infection and Immunity Programs in the Monash Biomedicine Discovery Institute (BDI), and Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia. Trained as a bioinformatician and data-savvy scientist, he has a very strong specialty in Artificial Intelligence, Bioinformatics, Comparative Genomics, Cancer Genomics, Bacterial Genomics, Computational Biomedicine, Data Mining, Infection and Immunity, Machine Learning, Proteomics, and Biomedical Big Data Analytics, which are highly sought-after expertise and skill sets in data-driven, paradigm-shifting biomedical research.
He is Head of the AI-driven Bioinformatics and Biomedicine Laboratory in the Monash BDI and an Associate Investigator of the ARC Centre of Excellence in Advanced Molecular Imaging. He is also a member of the Monash Data Futures Institute (MDFI), Alliance for Digital Health at Monash (ADAM) and the Monash Bioinformatics Platform. His main research interests are bioinformatics, digital health, heterogeneous data modeling, machine learning, and data analytics in the fields of infection and immunity, and cancer biology/pathology. Jiangning is currently an Associate Editor of four top-tier bioinformatics and computational biology journals BMC Bioinformatics, Genomics, Proteomics & Bioinformatics, Frontiers in Bioinformatics, BMC Genomic Data and Protein & Peptide Letters. He is also an Editorial Board member of Computers in Biology and Medicine, Biomolecules, and Advisory Board Member of Current Protein & Peptide Science, Guest Editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Current Bioinformatics, Frontiers in Genetics, Frontiers in Developmental and Cell Biology, BMC Genomics and BMC Medical Genomics.