BioDM 2013 - 2013 Workshop on Biological Data Mining and its Applications in Healthcare

The biologists are stepping up their efforts to understand the biological processes that underlie disease pathways. This has resulted in a flood of biological and clinical data from genomic sequences, DNA microarrays, and protein interactions, to biomedical images, disease pathways, and electronic health records. We are in a situation where our ability to generate biomedical data has greatly surpassed our ability to mine and analyze the data.
We can expect data mining to play an increasingly crucial role in furthering biological research, since data mining is designed to handle challenging data analysis problems. In fact, it is our hope that data mining will be the next technical innovation employed by biologists to enable them to make insightful observations and groundbreaking discoveries from their wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.
There are still many fundamental data analysis challenges to be overcome in order to discover new knowledge from the biomedical data to translate into clinical applications. These include practical issues such as handling noisy and incomplete data (e.g. protein interactions have high false positive and false negative rates), processing compute-intensive tasks (e.g. large scale graph mining), and integrating heterogeneous data sources (e.g. linking genomic data, proteomics data with clinical databases).
This is an unprecedented opportunity for data mining researchers from the computer science domain to contribute to the meaningful scientific pursuit together with the biologists and clinical scientists. This workshop’s mission is to disseminate the research results and best practices of data mining approaches to the cross-disciplinary researchers and practitioners from both the data mining disciplines and the life sciences domains. We encourage submission of papers describing the design and use of data mining techniques to address the various challenging issues in biological data analysis. We particularly welcome paper submissions that report the development of data mining techniques in healthcare-related applications that integrate the use of biological data in a clinical context for translational research.
PC members
Zhang Aidong, State University of New York at Buffalo (UB), USA
Tatsuya Akutsu, Kyoto University, Japan
Zeyar Aung, Masdar Institute of Science and Technology, UAE
Vladimir Bajic, King Abdullah University of Science and Technology, Saudi Arabia
Jake Chen, Indiana University School of Informatics, Indianapolis, USA
Jin Chen, Michigan State University, USA
Honnian Chua, Institute for Infocomm Research, Singapore
Juan Cui, University of Georgia, USA
Yang Dai, University of Illinois at Chicago, USA
Aryya Gangopadhyay, University of Maryland, Baltimore County, USA
Xin Gao, King Abdullah University of Science and Technology, Saudi Arabia
Xiaoxu Han, Eastern Michigan University, USA
David Hansen, Australian e-Health Research Centre, Australia
Jun (Luke) Huan, University of Kansas, USA
Jimmy Huang, York University, Canada
Daisuke Kihara, Purdue University, USA
Chee Keong Kwoh, Nanyang Technological University, Singapore
Hiroshi Mamitsuka, Kyoto University, Japan
George Perry, University of Texas at San Antonio, USA
Mark A. Ragan, The University of Queensland, Australia
Raul Rabadan, Columbia University, USA
Jianhua Ruan, University of Texas at San Antonio, USA
Saeed Salem, North Dakota State University
Ambuj K Singh, University of California at Santa Barbara, USA
Narayanaswamy Srinivasan, Indian Institute of Science, India
Zeeshan Syed, University of Michigan, USA
Vincent S. Tseng, National Cheng Kung University, Taiwan
Alfonso Valencia, Spanish National Cancer Research Centre, Spain
Hong Yan, City University of Hong Kong, China
Sungroh Yoon, Seoul National University, Korea
Philip S. Yu, University of Illinois at Chicago, USA
Erliang Zeng, University of Notre Dame, USA
Xiaoling Zhang, Boston University, Boston, USA
Yongjin Li, The University of Texas at Dallas, USA
Marketa Zvelebil, Breaktrhough Breast Cancer Research - ICR, UK