Keynote and Plenary Speakers


Prof. David Zhang
Hong Kong Polytechnic University, Hong Kong


 

David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in both Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. Currently, he is a Chair Professor at the Hong Kong Polytechnic University where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government in 1998. He also serves as Visiting Chair Professor in Tsinghua University and HIT, and Adjunct Professor in Shanghai Jiao Tong University, Peking University, National University of Defense Technology and the University of Waterloo. He is the Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG); Book Editor, Springer International Series on Biometrics (KISB); Organizer, the first International Conference on Biometrics Authentication (ICBA); Associate Editor of more than ten international journals including IEEE Transactions and so on. So far, he has published over 20 monographs, 400 international journal papers and 40 patents from USA/Japan/HK/China. He has been continuously listed as a Highly Cited Researchers in Engineering by Clarivate Analytics (formerly known as Thomson Reuters) in 2014, 2015, 2016 and 2017, respectively. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR.

 

Speech Title:"Medical Biometrics"

 

Abstract: As one of the most powerful and reliable means of personal authentication, biometrics has been an area of particular interest. In this talk, we will explore how biometrics technology could be also applied to medical applications. By learning from the biometrics definition, medical biometrics could be implemented by measuring different human being’s surface information, extracting all possible features as their representations and making a correct final decision. As a case study, Traditional Chinese Medicine (TCM) diagnosis methods, including looking/smelling/touching/hearing sensing, are developed by using of medical biometrics. The experimental results have been illustrated the effectiveness of medical biometrics.

Prof. Yuan-Ting Zhang

The Key Lab for Health Informatics of Chinese Academy of Sciences, China

 

Prof. Yuan-Ting Zhang is the Director of Joint Research Center for Biomedical Engineering, Founding Head of the Division of Biomedical Engineering, and Professor of Department of Electronic Engineering at the Chinese University of Hong Kong. Dr. Zhang serves concurrently the Director of the Key Lab for Health Informatics of the Chinese Academy of Sciences (HICAS). His research spans several fields including wearable medical devices, body sensor networks, bio-THz technologies, bio-modeling, neural engineering, cardiovascular health informatics, and e-p-m-Heath and telemedicine technologies, and is closely tied up to his teaching and publishing activities. He has authored/co-authored over 400 scientific publications and 11 book chapters, and filed 31 patents. His research work has won him a number of Awards including the best journal paper awards from IEEE-EMBS and the Asia Pacific ICTA e-Health Award. Dr. Zhang provided extensively professional services of significant value to the local industries and global academic communities. He served as Associate Editor of IEEE Transactions on Biomedical Engineering, founding Associate Editor of IEEE Transactions on Mobile Computing, Guest Editor for IEEE Transactions on Information Technology in Biomedicine, and Guest Editor for IEEE Communication Magazine. He was previously the Vice-President of the IEEE-EMBS. He served as the Technical Program Chair and the General Conference Chair of the 20th and 27th IEEE-EMBS Annual International Conferences in 1998 and 2005, respectively. He was a member of IEEE Fellow Elevation Committee and the Award Committee for IEEE Medal on Innovations in Healthcare Technology.

 

Prof. Taesung Park

Seoul National University, South Korea

 

Prof. Taesung Park received his B.S. and M.S. degrees in Statistics from Seoul National University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a visiting professor in Department of Biostatistics at the University of Washington. From Sep. 1999 to Sep. 2001, he worked as an associate professor in Department of Statistics at SNU. Since Oct. 2001 he worked as a professor and currently the Director of the Bioinformatics and Biostatistics Lab. at SNU. He served as the chair of the bioinformatics Program from Apr. 2005 to Mar. 2008, and the chair of Department of Statistics of SNU from Sep. 2007 and Aug. 2009. He has served editorial board members and associate editors for the international journals including Genetic Epidemiology, Computational Statistics and Data Analysis, Biometrical Journal, and International journal of Data Mining and Bioinformatics. His research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and statistical genetics.

 

Speech Title: "Hierarchical Structural Component Analysis of Gene-Gene Interactions"

 

While many statistical approaches have been proposed to detect gene-gene interactions (GGI), most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, “Hierarchical structural CoMponent analysis of Gene-Gene Interactions” (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis (GSCA), and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index.

 

Prof. Jose Nacher

Toho University, Japan

 

Prof. Jose Nacher received his Ph.D. in Theoretical Physics from Valencia University. From 2003-2007 he was a postdoctoral research fellow at the Bioinformatics Center, Institute for Chemical Research (ICR), Kyoto University. He was awarded with a JSPS Research Fellowship at the ICR, Kyoto University (2005-2007). From 2007-2012, he was a Lecturer and an Associate Professor at the Department of Complex and Intelligent Systems, Future University, concurrently with a visiting Associate Professor appointment at the Bioinformatics Center, ICR, Kyoto University (2011-2102) and Future University (2012-2013), respectively. From 2012, he was an Associate Professor at the Department of Information Science, Toho University. Since 2016, he is a Professor at the Department of Information Science, Faculty of Science, Toho University. He is a reviewer of more than 30 international journals in his field, serves as an Editorial Review Board of the International Journal of Knowledge Discovery in Bioinformatics (IJKDB) since 2009, as an Editorial Board of the Computational Biology Journal since 2012 and as an Editorial Board Member of Scientific Reports NPG since 2015. Prof. Nacher Lab's bioinformatics research interests include the development and application of novel mathematical methods and algorithms in systems biology and complex biological networks.

 

Speech Title: "Recent Progress on Controllability Models for Analysing Biological Networks"

 

The increasing availability of biological data has allowed us to represent biological systems as networks, in which nodes are life molecules and edges denote biochemical interactions, from protein-protein interaction (PPI) networks to metabolic pathways. Recent developments on network analysis are shifting the focus on controllability features of complex systems. In particular, controllability of complex networks is aiming at integrating concepts from control theory and network science with the purpose of understanding and ultimately control large-scale networks. Several frameworks have been proposed to control complex networks and among them, the Maximum Matching (MM) and the Minimum Dominating Set (MDS) models have recently gained popularity and have been used in several biological network analyses. In this talk, we first present the MM and MDS frameworks and the main theoretical grounds on which the models are built. Then, we review the controllability analysis results of several biological networks, from protein-protein interactions networks to metabolic pathways and ncRNA-protein networks.

Prof. Sun Kim

Seoul National University, South Korea

 

Sun Kim is Professor in the School of Computer Science and Engineering, Director of Bioinformatics Institute, and an affiliated faculty for the Interdisciplinary Program in Bioinformatics at Seoul National University. Before joining SNU, he was Chair of Faculty Division C; Director of Center for Bioinformatics Research, an Associate Professor in School of Informatics and Computing; and an Adjunct Associate Professor of Cellular and Integrative Physiology, Medical Sciences Program at Indiana University (IU) Bloomington. Prior to joining IU in 2001, he worked at DuPont Central Research from 1998 to 2001, and at the University of Illinois at Urbana-Champaign from 1997 to 1998. Sun Kim received B.S and M.S and Ph.D in Computer Science from Seoul National University, KAIST and the University of Iowa, respectively.
Sun Kim is a recipient of Outstanding Junior Faculty Award at Indiana University 2004, US NSF CAREER Award DBI-0237901 from 2003 to 2008, and Achievement Award at DuPont Central Research in 2000. He is actively contributing to the bioinformatics community, serving on the editorial board for journals including editors for the METHODS journal and International Journal of Data Mining and Bioinformatics, having served on a board of directors for ACM SIG Bioinformatics and for education for the IEEE Computer Society Technical Committee on Bioinformatics. He has been co-organizing many scientific meetings including ACM BCB 2011 as a program co-chair, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2008 as a program co-chair and 2009 as a conference co-chair. In Korea, he is currently President of Korea Artificial Intelligence Society and Vice President of Korea Society of Bioinformatics and Systems Biology.

 

Speech Title:"Analysis of Omics Data: To Use Domain Knowledge or Not"

 

Abstract: Domain knowledge can be very helpful in analyzing genome-wide molecular data or omics data. However, incorporating domain knowledge into the computational framework is not straightforward. One popular and successful approach is to utilize curated biological networks for the analysis of omics data. These networks are constructed by utilizing literature and experimental data. A condition specific omics data measured under a specific condition can be mapped to biological networks and then various computational methods such as clustering and composition methods are used for the analysis of the condition specific data. We introduce some of the network-based analysis methods developed in my lab. Another important domain knowledge is the literature information on genes and biological pathways. The typical use of these literature information is for the interpretation of the omics data analysis results, but not for the analysis per se. Our group developed computational frameworks that can perform integrated analysis of experimental and literature data. While domain knowledge is important and useful, a typical situation is that we do not have enough domain knowledge since we as human did not create living organisms. In this case, we found that the popular deep learning technologies are very effective. In this talk, we will introduce some of the deep learning based analysis methods from my lab for the protein function prediction and breast cancer subtype classification.

 


Assoc. Prof. KUO-YUAN HWA
National Taipei University of Technology, Taiwan

 

Dr. Kuo-Yuan Hwa is an associate professor and the director of the Center for Biomedical Industries at the National Taipei University of Technology. Dr. Hwagraduated and received her PhD from the School of Medicine, the Johns Hopkins University. She is the president of the Medical Association for Indigenous Peoples of Taiwan (MAIPT). Dr. Hwa’s scientific interests are: 1) nanotechnology and biosensor, 2) new drug discovery for human diseases by proteomics and genomics approaches and 3) glycobiology, especially on enzymes kinetics. She has published 85 conference and journal articles and 10 patents. She has served in many national and international committees. Dr. Hwa has been invited as a speaker for many academic research institutes and universities in China, Korea, Japan and USA. She has been invited as a reviewer, a judge and an editor for international meetings and journals. In addition, one of her currently works is on developing culturally inclusive health science educational program, with both indigenous and western science knowledge for indigenous children. 

 

Speech Title:"Anti-Cancer Drug Discovery in the Era Precision Medicine- from Compounds to SNPs"

 

Abstract: According to the World Health Organization, cancer is the second leading cause of death globally. Globally, nearly 1 in 6 deaths is due to cancer, about 8.8 million deaths in 2015. Cancer is a group of diseases which begin with abnormal and uncontrollable cell division and growth. Neoplastic cells can convert into malignant tumors. Although many anticancer drugs have been developed over the past 50 years such as 5-Fluorouracil, there are still limitation. It is because when chemotherapy is carried out, normal cells in the body are also killed, that results many adverse effects. To develop more effective anticancer treatments i.e. targeted-based drugs is needed. Moreover, the majority of the first-in-class drugs approved by the FDA between 2009 to 2013 have been discovered through target-based approaches. We have previously isolated a series of anti-cancer compounds with defined mechanisms of actions via a comparative genomics approach. To further develop these compounds, here we have taken in silico approaches to find the specific SNPs in association with the efficacy of compounds by developing an analysis workflow. The identified SNPs can be further developed as complimentary screening tools for designing pre-clinical and clinical studies. The SNPs found in this studies can be used to recruit the correct patients for the drug development that would accelerate the drug discovery in the era of precision medicine.

 

Assoc. Prof. Naomichi Yamamoto
Seoul National University, South Korea

 

Dr. Naomichi Yamamoto is an Associate Professor in the Department of Environmental Health Sciences at Seoul National University. His current research interests include applications of molecular biology-based techniques to study risks and transports of indoor and atmospheric fungal bioaerosols. His primary research expertise is: i) aerosol science; ii) aerobiology; and iii) molecular fungal biology. His research team applies cutting-edged technologies such as next-generation sequencing (NGS) and RNA-Seq to study human health risks of pathogenic and allergenic fungal bioaerosols. Before joining to SNU faculty in 2012, Dr. Yamamoto worked at Yale School of Engineering as a postdoctoral scientist supported by the Japan Society for the Promotion of Science (JSPS). He received his B.Eng. degree in applied physics from Waseda University, M.S. degree in environmental health sciences from UCLA, and Ph.D. degree in environmental studies from the University of Tokyo.

 

Speech Title:"High-throughput Sequencing as a Tool to Analyze Medically Important Fungal Pathogens and Diversities"

 

Abstract: The kingdom Fungi is a diverse group of eukaryotic organisms with an estimated 1.5 million constituent species. Fungi discharge microscopic spores into air, and inhalation of fungal spores may cause respiratory illnesses such as allergies and opportunistic infections in human. Development of allergic diseases such as asthma is thought to be associated with the early-life exposures to environmental microbes and their diversities. Inhalation of pathogenic fungal spores may cause invasive fungal infections in immunocompromised individuals. To assess the risks of diseases caused by and associated with fungal pathogens and diversities, it is essential to correctly identify fungal pathogens down to the species level and accurately characterize diverse fungal communities in the environment. In this presentation, I introduce how high-throughput sequencing technologies and bioinformatics techniques are useful for accurately characterizing diverse fungal communities and correctly identifying fungal pathogens down to the species level, which are thought to be important when assessing the risks of fungal allergies and infections.

 

Assoc. Prof. Dongxiao Zhu

Wayne State University, USA

 

Dongxiao Zhu is currently an Associate Professor at Department of Computer Science, Wayne State University. He received his Ph.D. from University of Michigan. His current primary research interests are machine learning and data science with applications to health informatics and bioinformatics. Dr. Zhu has published over 50 peer-reviewed publications and numerous book chapters and he served on several editorial boards of scientific journals such as Scientific Reports, Plos one and BMC Genomics. Dr. Zhu's research has been supported by NIH, NSF and private agencies and he has served on multiple NIH and NSF grant review panels. Dr. Zhu has graduated numerous PhD and masters students and also mentored several postdoc fellows. Dr. Zhu’s teaching interest lies in biomedical informatics, algorithms, machine learning and data science.

 

Speech Title:"Multi-task Learning in Healthcare Analysis"

 

Abstract: Labeling machine learning instances is mostly a manual and costly process, e.g., one has to wait for the occurrence of the event of interest, which may not always be observed for every instance. When there are multiple related prediction tasks, we may benefit from the tasks relatedness. Simultaneously learning multiple related tasks, multi-task learning (MTL) provides a paradigm to alleviate labeled data insufficiency by bridging data from all tasks and improves generalization performance of all tasks involved. Even though MTL has been extensively studied, existing work investigating MTL for deep neuronal networks (DNNs) and survival analysis are very sparse. DNN effectively learns high-level, non-additive information from raw input features, which makes it promising in clinical research. To address the issue of scarcity in labeled data, we propose the use of Auxiliary-Task-Augmented Network (ATAN), a predictive model (for primary target) with introducing auxiliary tasks as regularization. ATAN leverages clinically relevant measures as auxiliary targets and learns the clinical relevance explicitly. For time-to-event outcomes, we propose a novel multi-task survival analysis framework that takes advantage of both censored instances and task relatedness. We develop efficient algorithms and demonstrate the performance of the proposed multi-task DNN and survival analysis models on a number of public cancer and heart disease datasets. Our results show that the proposed MTL approaches can significantly improve the prediction performance in both time-to-event outcome and DNN.