Invited Speakers

 

Prof. Sung Wing Kin, Ken                                                                                                                

National University of Singapore, Singapore

 

Prof. Dr. Wing-Kin Sung received both the B.Sc. and the Ph.D. degree in the Department of Computer Science from the University of Hong Kong in 1993, 1998, respectively. He is a professor in the Department of Computer Science, School of Computing, NUS. Also, he is a senior group leader in Genome Institute of Singapore. He has over 20 years experience in Algorithm and Bioinformatics research. He also teaches courses on bioinformatics for both undergraduate and postgraduate. He was conferred the 2003 FIT paper award (Japan), the 2006 National Science Award (Singapore), and the 2008 Young Researcher Award (NUS) for his research contribution in algorithm and bioinformatics.

 

Speech Title: "Improving CNV Calling from High-Throughput Sequencing Data through Statistical Testing"

 

Abstract: Structural variations (SV) are large scale mutations in a genome; although less frequent than point mutations, due to their large size they are responsible for more heritable differences between individuals. Two prominent classes of SVs are deletions and tandem duplications. They play important roles in many devastating genetic diseases, such as Smith-Magenis syndrome, Potocki-Lupski syndrome and Williams-Beuren syndrome. Since paired-end whole genome sequencing data has become widespread and affordable, reliably calling deletions and tandem duplications has been a major target in bioinformatics; unfortunately, the problem is far from being solved, since existing solutions often offer poor results when applied to real data. In this talk, we will focuses on detecting deletions and tandem duplications from paired next-generation sequencing data. We will discuss why deletions and tandem duplications are difficult to call. Then, we will propose a statistical method SurVIndel that outperforms existing methods on both simulated and real biological datasets.

Assoc. Prof. Jiangning Song                                                                                               

Monash University, Australia

 

Dr. Song is an Associate Professor and Group Leader in the Cancer and Infection and Immunity Programs in School of Biomedical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. Trained as a bioinformatician and data-savvy scientist, he has a very 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 highly sought-after expertise and skill sets in the data-driven biomedical sciences. He was awarded a four-year NHMRC Peter Doherty Biomedical Fellowship. He also received both the JSPS Long-term and Short-term Fellowships and did his postdoctoral research at the Bioinformatics Center, Kyoto University, Japan. 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 and Protein & Peptide Letters and serves as an Advisory Board member of Current Protein & Peptide Science.

 

Speech Title: "Leveraging the Power of Data-Driven Machine Learning Techniques to Address Significant Biomedical Classification Problems"

 

Abstract: Recent advances in high-throughput sequencing have significantly contributed to an ever-increasing gap between the number of gene products (‘proteins’) whose function is well characterized and those for which there is no functional annotation at all. Experimental techniques to determine the protein function are often expensive and time-consuming. Recently, machine-learning (ML) techniques based on statistical learning have provided efficient solutions to challenging problems of sequence classification or functional annotation that were previously considered difficult to address. In this talk, by combining our recent research progress, I will highlight some important developments in the prediction of two representative sequence labeling problems in computational biology based on the high-dimensional, noisy and redundant information derived from sequences and the 3D structure. I will illustrate how ML methods can extract the predictive power from a variety of features that are derived from different aspects of the data and useful strategies that help to contribute to the predictive performance of ML approaches.

 

 

 

Previous Speakers

 

Prof. Wen-Lian Hsu
Academia Sinica, Taiwan
Prof. Kenta Nakai
The University of Tokyo, Japan
Prof. Keimei Oh
Akita Prefectual University, Japan
Prof. Manoj R. Tarambale
Marathwada Mitra Mandal’s College of Engineering, India
Assoc. Prof. Siew Woh Choo
Xi'an Jiatong-Liverpool University, China
Assoc. Prof. Riichi Kajiwara
Meiji University, Japan
   
Prof. Tatsuya Akutsu
Kyoto University, Japan
Distinguished Prof. Cuie Wen
RMIT University, Australia
Assoc. Prof. Hiroyuki Kudo
Meiji University, Japan
Assoc. Prof. Yuncang Li
RMIT University, Australia