Workshop on Data Mining in Life Sciences´2006 July 13, 2006, Leipzig/Germany
Workshop Chair 数据挖掘实验室
Isabelle Bichindaritz,University of Washington, USA 数据挖掘研究院
Workshop Committee 数据挖掘实验室
- Agnar Aamodt, Norwegian University of Science and Technology, Norway
- Tatsuya Akutsu, Kyoto University, Japan
- Kung-Ma Chao, National Taiwan University, Taiwan
- Michel Dojat, UM INSERM-UJF U594 , France
- Peter Funk, Malardalen University, Sweden
- Stefan Kramer, Technische University Munchen, Germany
- Xiaoqiu Huang, Iowa state University, USA
- Jingchu Luo, Peking University, China
- Stefania Montani, University of Piemonte Orientale, Italy
- Petra Perner, Institute of Computer Vision and Applied Computer Sciences, Germany
- Rainer Schmidt, Institut fur Medizinische Informatik und Biometrie, Germany
- Malika Smail-Tabbone, LORIA, France
- Yingwei Wang, University of Prince Edward Island, Canada
Scope of the Workshop 数据挖掘研究院
Data mining in biology and medicine is a core component of biomedical informatics, and one of the first intensive applications of computer science to this field, whether at the clinic, the laboratory, or the research center. Following a long tradition of data exploration stemming from biostatistical data analysis, todays′s biomedical data mining appears more multifaceted with advances in knowledge discovery in databases as well as machine learning approaches. 数据挖掘研究院
The goals of this workshop are to:
- provide a forum for identifying important contributions and opportunities for research on data mining as it applies to biological and/or medical data,
- promote the systematic study of how to apply data mining to biology and medicine, and
- show case applications of data mining in biology and medicine.
Some of the technical issues addressed, and potential outcomes of the workshop, are to identify preferred types of mining methods, tools, and processes, preferred domains of application, how to connect a data mining model with a problem to solve, challenges specific to applying data mining to biology and medicine, and guidelines to better develop data mining projects in this domain. We welcome all those interested in the problems and promise of data mining in biology or medicine.
Topics of interest include (but are not limited to):
- Discovery of high-level structures such as association networks
- Text mining from biomedical literature
- Medical images mining
- Data mining in bioinformatics (molecular biology, genomics, proteomics, pylogenetic classification)
- Data mining project development methodology for biomedicine
- Integration of data mining in the clinic
- Data mining in the Human Genome Project
- Temporal data mining
- Biomedical signals mining
- Data mining in agriculture
- Data mining in environmental sciences.
Paper presentations will be interspersed with discussionsin which we characterize, categorize, and discuss the benefitsof specific CBR applications in the health sciences. A wrap-upround table discussion will summarize the lessons learnt, issues identified, and future directions. 数据挖掘研究院
Submission Requirements
PostScript (compressed and uuencoded) or PDF paper submissions should be formatted according to Springer LNCS format, with a maximum of ten pages. Author′s instructions along with LaTeX and Word macro files are available on the web at http://www.springer.de/comp/lncs/authors.html. 数据挖掘研究院
Please e-mail your submission Isabelle Bichindaritz at ibichind@u.washington.edu.
