Submission Deadline: April 10, 2017
Registration Deadline: April 30, 2017
Camera-Ready Paper Due: April 30,
Conference Dates: May 24-26, 2017
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Prof. Saman K. Halgamuge
The Australian National University, Australia
Prof. Saman Halgamuge is the Director/Head, Research School of
Engineering, Australian National University. He is elevated to IEEE
Fellowship from January 2017. He has held appointments as Professor and
Associate Dean International at the University of Melbourne. He
graduated with Dipl.-Ing and PhD degrees in Data Engineering
(“Datentechnik”) from Technical University of Darmstadt, Germany. He has
an outstanding research record in Data engineering, which includes Big
Data Analytics and Optimization focusing on applications in
Mechatronics, Energy and Bioengineering. He has been a frequently
invited public speaker and delivered about 20 keynote speeches
He completed supervision of 32 PhD students and currently supervises a group of 15 PhD students. He is an Associate Editor of BMC Bioinformatics and IEEE Transactions on Circuits and Systems II: Express Letters and founding co-editor of Frontier’s journal on Mechanical Engineering- Mechatronics section. He published over 250 research papers including a research book, 6 edited books, 20 book chapters, 100 journal articles, and over 130 refereed conference papers. He is a member of Australian Research Council (ARC) College of Experts panel for Engineering, Information and Computing Sciences. His publication profile is at http://scholar.google.com.au/citations?sortby=pubdate&hl=en&user=9cafqywAAAAJ&view_op=list_works
Speech Title: Inferring Drug Interactions Using Data Analytics
Abstract: Repositioning of existing drugs as appropriate medication for previously not associated medical conditions can reduce the time, costs and risks of drug development by identifying new therapeutic effects. Investigating and understanding the interactions between drugs as well as how they work on our body is important in improving the effectiveness of clinical care. In the first part of the talk, recent studies on the interaction between drugs  and drug repositioning  are presented. A method based on Positive Unlabelled Learning and Growing Self Organising Maps is used on data available in DrugBank database to infer drug-drug interactions. The proposed approach was able to infer 5892 drug pairs that are highly likely to interact with each other . Subnetwork identification has already been used to simplify the visualization and interpretation of data in biological networks, but it has not been applied to drug repositioning. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed to solve drug repositioning . Drug Similarity Networks are generated using chemical, therapeutic, protein, and phenotype features of drugs and Anatomical Therapeutic Chemical classification .
In the second part, characterisation of drugs using Multi-Electrode Arrays (MEA) is discussed [3-4]. MEA is an extracellular recording technology that enables the analysis of networks of neurons in vitro. Neurons in culture exhibit a range of behavioral dynamics, which can be measured in terms of individual action potentials, network-wide synchronized firing and a host of other features that characterize network activity. MEA data analysis is used to differentiate between two types of antiepileptic drugs with different mechanisms of action.
Acknowledgement: Prof Karin Verspoor and Dr Snezana Kuslijc of University of Melbourne and Prof Steve Petrou of Howard Florey Institute, current PhD students Dulini Mendis, Emma Morrisroe, Nusrath Hameed and Yahui Sun participated in the published research referred to in this talk. The funding organisation Australian Research Council and the PhD scholarships provided by University of Melbourne are also acknowledged.
The following research papers cover the content of the presentation:
 PN Hameed, K Verspoor, S Kusljic, S Halgamuge, “Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes” BMC bioinformatics 18 (1), 2017
 ] Y. Sun, PN. Hameed, K. Verspoor and S. K. Halgamuge, “A Physarum-inspired Prize-Collecting SteinerTree approach to identify subnetworks for drug repositioning”, BMC Systems Biology, 2016.
 D. C. Mendis, E. Morrisroe, S. Petrou, S.K. Halgamuge, “Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation", Journal of Neural Engineering, 2016, 13(2):026009
 D. C. Mendis, S. Petrou and S. K. Halgamuge, “Neuromechatronics with In-Vitro Microelectrode Arrays”, C.W. de Silva, F. Khoshnoud, L. Maoqing and S. K Halgamuge (Editors), ”Mechatronics: Fundamentals and Applications'', Taylor & Francis, 2015.
Prof. Yoshifumi Manabe
Faculty of Informatics, Kogakuin University, Tokyo, Japan
Yoshifumi Manabe was born in 1960. He received his B.E., M.E., and Dr.E. degrees from Osaka University, Osaka, Japan, in 1983, 1985, and 1993, respectively. From 1985 to 2013, he worked for Nippon Telegraph and Telephone Corporation. From 2001 to 2013, he was a guest associate professor of Graduate School of Informatics, Kyoto University. Since 2013, he has been a professor of the Faculty of Informatics, Kogakuin University, Tokyo, Japan. His research interests include distributed algorithms, cryptography, game theory, and graph theory. Dr. Manabe is a member of ACM, IEEE, IEICE, IPSJ, and JSIAM.
Speech Title: Fair Allocation Problems
Abstract: Fair allocation problem is one of the most fundamental problems in economics, game theory, multi-agent systems, computer science, and our daily life. The problem definition is very simple. There is a good (or a set of goods) to be shared among a number of people. Find an allocation that is fair among the people. Though the problem is old and very simple, many researches have been done and the problem is still considered. There are two types of problems according to whether the good is divisible or not. This talk introduces old and new results on the fair allocation protocols and shows a new direction, online version, of the problem.