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Keynote&Invited Speakers

In order to deepen the communication in all the participants, ICCBB 2019 have invited professors from local Japan and all over the world to have speeches about Computational Biology and Bioinformatics and related fields.


Keynote Speaker I

Prof. Kevin Downard, University of New South Wales, Australia

Kevin Downard conducted his postdoctoral studies and held a subsequent academic position at the Massachusetts Institute of Technology after the award of his PhD degree from the University of Adelaide in Australia. For the past 22 years he has held professorial academic positions in the USA and Australia and is currently appointed in the Faculty of Medicine at the University of New South Wales in Sydney. A focus of his research is improving responses to infectious disease causing pathogens using new diagnostic and therapeutic approaches. He has over published over 100 articles as lead author as well as two books. He is internationally recognized in his field of specialty, mass spectrometry, and has received awards from both the American and British Mass Spectrometry Societies as well as the Australian Academy of Science and the Japan Society for the Promotion of Science. He also holds an Honorary Professorship at Yokohama City University.

Speech Title: "Studies in Evolutionary Biology using a New Mass-based Phylonumerics Approach and Algorithm"

Abstract: Molecular based studies in evolutionary biology have almost exclusively used gene sequence data. A new, numerical mass based protein phylogeny approach, known as phylonumerics, has been developed that employs a purpose built algorithm to build phylogenetic-like trees. These trees are constructed from sets of numerical mass map data from the digestion of an expressed protein, without the need for either gene or protein sequences. Such trees have been shown to be highly congruent with sequence based trees and provide a reliable means to study the evolution history of any organism. Furthermore, single point mutations can determined from the differences in mass of peptide pairs of different mass sets and displayed along branches across the tree. This presentation will describe the basis of the approach and its application to investigate the evolution of the influenza virus. Frequent ancestral and descendant mutations that precede and follow the manifestation of antiviral resistance mutations in influenza neuraminidase have been identified to establish how strains develop resistance to antiviral drugs. Since such mutations usually impart some cost to viral fitness, the approach also allows co-occurring, or consecutive or near consecutive, epistatic and compensatory mutations important to the survival of the virus to be studied.

Keynote Speaker II

Assoc. Prof. Ahmed Moustafa, Nagoya Institute of Technology, Japan

Dr. Ahmed Moustafa received his PhD from University of Wollongong in Australia. He is an Associate Professor of Department of Computer Science, Graduate School of Engineering, Nagoya Institute of Technology, Japan. He is a member of the Japan Society of Artificial Intelligence, IEEE Computer Society, Australia Computer Society, Service Science Society of Australia. He was a visiting researcher in University of Adelaide, Auckland University of Technology and Data61, Australia. His main research interests include complex automated negotiation, multiagent reinforcement learning, trust and reputation in multiagent societies, deep reinforcement learning, service oriented computing, collective intelligence, intelligent transportation systems and data mining. He served as a PC member in many reputed conferences including ICWS, ICSOC and WWW.

Invited Speaker I

Assoc. Prof. Dakun Lai, University of Electronic Science and Technology of China, China

Dr. Lai is currently the director of the Biomedical Imaging and Electrophysiology Lab at the University of Electronic Science and Technology of China (UESTC). He received his Ph.D. in Medical Electronics from Fudan University in 2008. Then he completed a three-year Postdoctoral Associate in Biomedical Engineering at the University of Minnesota, USA. From 2012, he has been on the faculty of the School of Electronic Science and Technology, UESTC, China, where he was appointed as an Associate Professor of Electrical Science and Technology. Dr. Lai is members of IEEE and the Engineering in Medicine and Biology Society, and the member of American Heart Associate. He has served as a peer reviewer of IEEE Transction on Biomedical Egnineering, IEEE ACCESS, and related Chinese Journals. He has publised 30 peer-reviewed papers in Circulation, Physics in Medicine and Biology, IEEE Transcation on Information Technology in Biomedicine etc. and holds 20 Chinese Patents. His research interests and main contributions include computational medicine and deep learning, bioelelctromagnetics and medical applications, automated detection and prediction cardiac/neruo electrical disorder.

Speech Title: "Automated Detection and Prediction of Serious Cardiac Electrical
Disorder by Using Machine Learning"

Abstract: Artificial intelligence has transformed key features of human life. Machine learning is a subset of artificial intelligence in which machines autonomously acquire information by extracting patterns from large databases. It has been progressively used in the medical science and clinical diagnosis especially within the domain of cardiac electrical disorders, such as precise detection of cardiac electrical arrhythmias and further earlier prediction of such serious diseases as sudden cardiac death. Compared with manual analysis and diagnosis in past, it shows great superiority under such current mass clinical bio-signal data, which is promoted by the modern fast communication technology and advanced wearable long-term monitoring systems. Modern machine learning models can automatically identify different electrocardiograms (ECG) with high precision; moreover automatically extract all interested features and clinically significant parameters. Several deep learning models have been developed for the high fidelity detection of common rhythm disturbances as in case of atrial fibrillation and complex arrhythmias. Here, we have highlighted numerous applications of machine learning for prediction and early detection of cardiac electrical disorders. Machine learning algorithms try to develop the model by using all the available input. In future it will be used for more healthcare areas to improve the quality of diagnosis.



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Contact Us

The secretary office of ICCBB 2019 will collect contributions and finish daily organizing work. All paper review process will be completed by Program Committee and Technical Committee Members.

If you have any question, please feel free to contact our conference secretary.

Ms. Olia Lai

Email: iccbb@cbees.net

Tel.: +852-3500-0137 (Hong Kong)/+86-28-86528465 (China)

Working Time: Monday-Friday, 9:30-18:00 (UTC/GMT+08:00)  

In order to deepen the communication in all the participants, ICCBB 2019 is welcomed experts and scholars from all over the world to join in the conference committee.

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Important Dates

Paper Submission (Full Paper) Before Aug. 20, 2019
Paper Submission (Abstract) Before Aug. 20, 2019
Notification of Acceptance On Sept. 05, 2019
Registration Deadline Before Sept. 15, 2019    
Conference Dates On Oct. 17-18, 2019
Academic Visit On October 19, 2019

Organized by                                                  Supported by


                  

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