KEYNOTES
KEYNOTE 1
DR KHANG TSUNG FEI
Senior Lecturer at Institute of Mathematical Sciences, Faculty of Science, University of Malaya
Dr Khang Tsung Fei is a statistician with strong interests in the development and application and development of statistical methods in data science. His research interest broadly covers the area of bioinformatics, ecology, medical statistics, and machine learning. Since 2010, he has worked on over 20 projects as the principal consultant statistician, and conducted numerous workshops on statistics, bioinformatics, and R programming. Currently, he heads the UM Centre for Data Analytics, which focuses on establishing collaborative research and engagement between the industry and academia in Malaysia.
Machine Learning for Biology
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The pace, volume and complexity of biological data currently generated is unprecedented in the history of biology. Consequently, opportunities are wide open to data enthusiasts to mine this rich source of data, whether in solo (good) or in collaboration with biologists (better). Machine learning methods are widely used in bioinformatics for classification of meaningful biological states, such as disease states, species identity, etc. In this talk, I will take you through a tour of some of my works that involve the use of machine learning methods to produce meaningful solutions to biological problems. We will also discuss problems associated with over ambitious interpretation of machine learning results in biology that have become increasingly common in the literature.
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ASSOF PROF DR CHOI SY BING
Chief Executive Officer of Alterquo Sdn. Bhd.
Associate Professor at Perdana University
KEYNOTE 2
Dr. Choi is the Chief Executive Officer of Alterquo Sdn. Bhd. and an Associate Professor at Perdana University. With a degree in Biochemistry from Universiti Malaya (UM), she pursued further studies with MSc in Information Technology and PhD in Pharmaceutical Technology from Universiti Sains Malaysia (USM). She was an awardee of USM Postgraduate Fellowship and JASSO scholarship during her studies. In addition, she was selected to participate in the Frontierlab@osakaU Scientific Empowerment Programme in Japan. Besides varied research experience, she was the facilitator for International Students from University California San Diego (United States), Osaka University (Japan), Indian
Institute of Technology Delhi (India), Chulalongkorn University (Thailand) and many more prestigious universities around the world, under the PRAGMA programme. Prior to her appointment at Perdana University, she was a research scientist at the Natural Product and Drug Discovery Centre of the Malaysian Institute of Pharmaceuticals and Nutraceuticals (IPharm) and a postdoctoral research fellow at the School of Industrial Technology, USM. Her expertise include protein structure prediction, membrane protein molecular dynamics simulation, and in silico high throughput screening, as well as structure and ligand-based drug discovery.
Leveraging Molecular Modelling to Understand COVID
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During the pandemic, scientist and researcher were facing challenges in conducting conventional laboratory-based research during the lockdown. Bioinformatics is now more than ever becoming forefront for them to continue their research. The usage of bioinformatics tools to analyse data from genomics, transcriptomics, proteomics as well as structural biology had become critical in deciphering the molecular characterizations of infectious pathogens. The focus of this talk would be on molecular modelling, one of the computational approaches that is used significantly during this pandemic to understand the dynamics behaviour of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) drug and predicting effective treatment for COVID research.