Since the advent of the Human Genome Project, a great deal of interest has been centred on the applications of large scale "omics" data in biomedical research, with the ultimate goal of enabling personalized treatments of human diseases. Inadvertently, such "omics"-based research present significant challenges for biomedical researchers, often leaving them questioning how they could intelligently extract, harness and derive actionable knowledge from the bewildering amount of data. Data mining techniques - such as machine learning - can be used to uncover meaningful patterns from complex biomedical datasets and derive useful predictions for knowledge discovery. Biomedicine analytics involve theoretical concepts and practical solutions spanning a wide range of domains - such as cellular biology, immunology and neuroimaging.
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Speaker's Biography
Dr. Lawrence Wee is a research scientist at the Institute for Infocomm Research, specializing in the application of data mining techniques for basic and translational biomedical research. He has developed several novel techniques and proprietary software for analyzing and modelling biomedical data, and is currently involved in a number of collaborative research with bench scientists and clinicians globally. His services to the scientific community include reviewing submissions to top-tiered bioinformatics journals and conferences, as well as organizing local and international bioinformatics conferences, meetings and workshops. Dr Wee obtained his Ph.D. from the National University of Singapore. He is also a member of the International Society for Computational Biology (ISCB) and the Association for Medical and Bio-informatics Singapore (AMBIS).
