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                                                     Joint Seminar

                                                             by

             Department of Systems Engineering and Engineering Management

                                                             and

                                                Faculty of Science
                                  The Chinese University of Hong Kong

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Title	:	Knowledge Discovery and Management in Life Sciences
Speaker	:	Dr. Fazel Famili
		Knowledge Discovery (KD) Group
		Institute for Information Technology, National Research Council of Canada, Ottawa, Canada
Date	:	November 3rd, 2009 (Tuesday)
Time	:	4:30 p.m. - 5:30 p.m.
Venue	:	***ERB LT***
		William M.W. Mong Engineering Building
		(Engineering Building Complex Phase 2)
		CUHK

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Abstract:
　

Knowledge discovery has emerged as a fundamental solution in understanding the real value of large amounts of data that we collect. Particular examples are related to life sciences, physical systems (e.g. sensor-based systems) and financial domain. Of the more complex of these examples is the life sciences domain where one tries to integrate and analyze large amounts of high-throughput genomics and proteomics data obtained from either single time point or time-series applications. Similar to many other domains, in life sciences, various methods have also been developed, and many data mining tools (commercial, non-commercial) have been introduced. These applications have all contributed to: (i) identification of certain genes or proteins and their functions, (ii) gene response analysis in biological studies, such in-vitro, in-vivo or x-vivo, research and (iii) understanding the molecular mechanism of certain species and their associated biological pathways. This wealth of newly discovered and existing knowledge has prompted a question: what is the best way to properly manage all discovered knowledge, when it is validated. This question has also been one of the motivations behind several data mining research projects that we have initiated in the KD group. Here, in addition to searching for patterns in genomics and proteomics data, we have been working to identify proper ways to represent, structure, and distribute all forms of knowledge, most preferably taking an AI approach.

This talk consists of two parts. In part one, we provide an overview of knowledge discovery focusing on life sciences and describe the main motivations for developing and applying knowledge discovery methods to analyze complex biological data. We also briefly describe a few of our case studies where we have analyzed high throughput biological data using unsupervised or supervised machine learning techniques. These are cases in which real biological data sets (obtained from public or private sources) have been analyzed and studied for tasks such as gene function identification and gene response analysis. In part two of this talk, we describe how discovered and validated knowledge could be structured into knowledge bases where it can be integrated with other forms of knowledge, for dissemination to multiple users. We conclude our talk with some lessons learned and the research directions that we are currently pursuing.

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Biography:
　

Dr. A. Famili is a Senior Research Scientist, Group Leader for the Knowledge Discovery Group and a leading data mining expert working at the Institute for Information Technology (IIT) of the National Research Council of Canada (NRC), where he has been for the last 24 years. Prior to joining NRC, he worked in industry for 3 years. Dr. Famili has been actively involved in the field of Artificial Intelligence, Data Mining and Bioinformatics and successful application of these technologies. He has a strong data mining and bioinformatics team within IIT that is currently engaged in unique research and development in data mining for genomics, proteomics and health care. His research has been on data mining, machine learning and bioinformatics and their applications to real world problems in various data rich environments, such as sensor-based systems and life sciences. Dr.
Famili has edited two books, published over 50 articles in the area of data mining and AI and holds a US data mining patent. He has organized many workshops, has been involved in a number of data mining and AI conferences and has extensive collaboration with a number of institutes in Canada, Europe and South America. He is also on the editorial board of four scientific journals and an adjunct professor at SITE (School of Information Technology and Engineering), and The Institute of System Biology, at the University of Ottawa.

************************* ALL ARE WELCOME ************************

Host	:	Prof. Kai Pui Lam
Tel	:	(852) 2609-8330
Email	:	kplam@se.cuhk.edu.hk
Enquiries	:	Prof. Nan Chen or Prof. Sean X. Zhou
	:	Department of Systems Engineering and Engineering Management
		CUHK
Website	:	http://www.se.cuhk.edu.hk/~seg5810
Email	:	seg5810@se.cuhk.edu.hk

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