Computational Knowledge Discovery for Bioinformatics Research

Computational Knowledge Discovery for Bioinformatics Research

Xiao-Li Li (Institute for Infocomm Research, A* STAR, Singapore) and See-Kiong Ng (Institute for Infocomm Research, A* STAR, Singapore)
Release Date: June, 2012|Copyright: © 2012 |Pages: 463
ISBN13: 9781466617858|ISBN10: 1466617853|EISBN13: 9781466617865|DOI: 10.4018/978-1-4666-1785-8

Description

Biological and clinical studies provide valuable insight into the causes and potential cures of disease. Using statistics and data mining and other computational approaches, bioinformatics researchers can provide the medical community with ground-breaking discoveries that change how we perceive and treat these illnesses.

Computational Knowledge Discovery for Bioinformatics Research discusses the most significant research and latest practices in computational knowledge discovery approaches to bioinformatics in a cross-disciplinary manner which is useful for researchers, practitioners, academicians, mathematicians, statisticians, and computer scientists involved in the many facets of bioinformatics. This book aims to increase the awareness of interesting and challenging biomedical problems and to inspire new knowledge discovery solutions.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Bioinformatics databases
  • Biological data management
  • Biological knowledge discovery
  • Clinical research informatics
  • Clustering Techniques
  • Gene expression analysis
  • Graph Mining
  • Protein interaction networks
  • Protein/RNA structure prediction
  • Translational bioinformatics

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Xiao-Li Li is currently a principal investigator in the Data Mining Department at the Institute for Infocomm Research, A*Star. He also holds an appointment of adjunct assistant professor in SCE, NTU. Xiao-Li received his PhD degree in computer science from Chinese Academy of Sciences (2001) and was then with National University of Singapore (School of Computing/Singapore-MIT Alliance) as a research fellow from 2001 to 2004. His research interests include bioinformatics, data mining, and machine learning. He has been serving as a member of technical program committees in numerous bioinformatics (a book editor for Biological Data Mining in Protein Interaction Networks, PC members for IEEE BIBE, IEEE BIBM, etc.), data mining (including a PC member in leading data mining conference KDD, CIKM, and SDM), and machine learning related conferences (a session chair of PKDD/ECML). He has also served as an editorial board member for International Journal of Data Analysis Techniques and Strategies (IJDATS), Journal of Information Technology Research (JITR) and other IGI Global editorial advisory review boards. In 2005, he received best paper award in the 16th International Conference on genome informatics (GIW 2005). In 2008, he received the best poster award in the 12th Annual International Conference Research in computational molecular biology (RECOMB 2008). To learn more about Dr. Xiao-Li Li, please visit his Web page: http://www1.i2r.a-star.edu.sg/~xlli/.
See-Kiong Ng is currently the Department Head of the Data Mining Department at the Institute for Infocomm Research. He is also an adjunct associate professor at the School of Computer Engineering, Nanyang Technological University. Dr. Ng obtained his PhD in computer science from Carnegie Mellon University. He wrote the TrueAllele software when he was a graduate student at CMU. The program was eventually used by a biotech company in Iceland to genotype the entire Icelandic population, thereby beginning his brave journey into the exciting field of genomics as a computer scientist. Dr. Ng's current research focuses on unraveling the underlying functional mechanisms of protein interaction networks as well as other real-world networks. His continuing and emerging diverse and cross-disciplinary research interests include bioinformatics, text mining, social network mining, and privacy-preserving data mining.