Biomarker discovery is an important topic in biomedicine that may lead to significant breakthrough in wide applications like disease analysis and target therapy. For biomarkers, we refer to biological entities whose alterations are measurable and characteristic of a biological phenotype. In biomedical sciences, with advancing genomics, proteomics, metabolic, and systems biology techniques, many molecular entities such as genes, RNAs, proteins, metabolites have been examined as candidate biomarkers of disease. Managing, interpreting, and discovering knowledge of new biomarkers have been kept as challenging and attractive issues in this emerging field of biomedical informatics.

The Workshop on Data Mining for Biomarker Discovery (DMBD) aims to provide a premier forum for sharing state-of-the-art computational techniques for biomarker discovery. We welcome both computational and biomedical research papers that are previously unpublished, with particular emphasis on the following areas:

• Biomarker data integration and information retrieval methods

• Large open-access biomarker databases information management systems

• Statistical machine learning techniques in biomarker discovery

• Functional genomics techniques that links gene expressions to disease
phenotypes

• Computational proteomics for clinical applications

• Mining biomarkers in Mass spectrum data

• Structure analysis of biomarker molecules

• Sequence and structural motif finding to understand molecular mechanism of
biomarkers

• RNAi and microRNA biomarker discovery

• Comparative genomics studies of biomarkers

• Modeling of biological pathways and networks involving biomarkers

• Biomedical text mining for biomarker finding

• Integrated clinical data mining and molecular data mining for biomarkers

• Drug discoveries and biomarkers

• Pharmacogenomics and biomarkers

• Personalized medicine and biomarkers

• Frameworks integrating computational and experimental biomarker knowledge
discoveries

• Case studies in biomarker discovery

• Open source libraries, portals, and utilities for biomarker discovery

• Ethics and privacy issues in mining biomarkers for patient data



SUBMISSION:

Please submit a full-length paper (6 page IEEE 2-column format) through the online submission system at http://kis-lab.com/cyberchair/bibm09/cbc_index.html. The format instruction can be downloaded from the BIBM’09 website http://www.ittc.ku.edu/bioinformatics/BIBM09/call.html. Electronic submissions (in PDF or Postscript format) are required.

Every paper will receive 2-3 reviews from the program committee, if the content is deemed relevant to the workshop theme. Each paper will be judged primarily on computational innovation, likely impact to biology and biomedicine for outcomes presented, and clarity of presentation. Exceptionally novel techniques, ideas, and/or breaking outcomes will be evaluated by the program chairs for a best paper award.



PUBLICATION:

The workshop proceeding will be made available online. Selected extended papers from the workshop will be invited for consideration for publication in a special issue of International Journal of Data Mining and Bioinformatics (SCI indexed) or an edited book volume to be announced after the workshop.



Important Dates:

• Submission deadline: Aug. 10, 2009

• Author notification: Sep. 10, 2009

• Camera ready papers: Sep. 17, 2009

• Workshop Dates: Nov. 1-4, 2009
(Exact date will be updated on the website)



Workshop Organizers:

Workshop Co-Chairs:

• Vincent S. Tseng, National Cheng Kung University, Taiwan
tsengsm@mail.ncku.edu.tw

• Hui-Huang Hsu, Tamkang University, Taiwan
huihuanghsu@gmail.com

• Jake Chen, Indiana University-Purdue University at Indianapolis, USA
jakechen@iupui.edu

Program Committee:

• Tatsuya Akutsu, Kyoto University, Japan

• Masanori Arita, University of Tokyo, Japan

• Bruce Aronow, Cincinnati Children’s Hospital, USA

• Daniel G. Brown, University of Waterloo, Canada

• Keith C. C. Chan, The Hong Kong Polytechnic Univ., China

• Wen-Lian Hsu, Academia Sinica, Taiwan

• Jun Huan, University of Kansas, USA

• David P. Kreil, Boku University Vienna, Austria

• Dan Li, Eli Lilly and Company, USA

• Simon Lin, Northwestern University, USA

• Martin Middendorf, University of Leipzig, Germany

• Chad Myers, University of Minnesota, USA

• Tun-Wen Pai, National Taiwan Ocean University, Taiwan

• Lusheng Wang, City University of HongKong, China

• Limsoon Wong, National University of Singapore, Singapore

• Ueng-Cheng Yang, National Yang-Ming University, Taiwan

• Weixiong Zhang, Washington University St. Louis, USA