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Second European Workshop on Data Mining and Text Mining for Bioinformatics

24 September 2004, Pisa, Italy

in conjunction with ECML/PKDD 2004: The Fifteenth European Conference on Machine Learning (ECML) and The Eighth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 20-24 September, 2004, Pisa, Italy.

Abstract

In the past years, research in molecular biology and molecular medicine has accumulated enormous amounts of data. This includes genomic sequences gathered by the Human Genome Project, gene expression data from microarray experiments, protein identification and quantification data from proteomics experiments, and SNP data from high-throughput SNP arrays. However, our understanding of the biological processes underlying these data lags far behind. There is a strong interest in employing methods of knowledge discovery and data mining to generate models of biological systems. Mining biological databases imposes challenges which knowledge discovery and data mining have to address and which will be in the focus of the workshop.

• Important background knowledge in bioinformatics is often buried in textual documents, such as scientific publi-cations or database annotations. Text mining and information extraction approaches currently being studied range from term recognition to extraction of complex relationships of interaction between proteins.
• Analysing data from biological databases often requires the consideration of data from multiple relations rather than from one single table. Recently, approaches (such as propositionalization algorithms) are being studied that utilize multi-relational data and yet meet the efficiency requirements of large-scale data mining problems.
• It is difficult and requires profound understanding of both knowledge discovery and computational biology to identify problems and optimization criteria which, when maximized by knowledge discovery algorithms, actually contribute to a better understanding of biological systems. Identification of appropriate knowledge discovery problems and development of evaluation methods for knowledge discovery results are ongoing efforts.

Goals and Intended Audience

Program

Proceedings

• Cover, table of contents, and invited talks by Yves Moreau and Alfonso Valencia.
• Hong Chai and Carlotta Domeniconi: An Evaluation of Gene Selection Methods for Multi-Class Microarray Data Classification.
• Nikolai Daraselia, Sergei Egorov, Andrey Yazhuk, Svetlana Novichkova,
  Anton Yuryev, and Ilya Mazo: Extracting Protein Function Information from MEDLINE Using a Full-Sentence Parser.
• Yoshikazu Kaneta, Md. Ahaduzzaman Munna, and Takenao Ohkawa: A Method of Extracting Sentences Related to Protein Interaction from Literature using a Structure Database.
• Svetlana Kiritchenko, Stan Matwin, and A. Fazel Famili: Hierarchical Text Categorization as a Tool of Associating Genes with Gene Ontology Codes.
• Judice L.Y.Koh, Mong Li Lee, Asif M. Khan, Paul T.J. Tan, and Vladimir Brusic: Duplicate Detection in Biological Data using Association Rule Mining.
• Andrea Malossini, Enrico Blanzieri, Raymond T. Ng: Assessment of SVM Reliability for Microarrays Data Analysis.
• Brigitte Mathiak and Silke Eckstein: Five Steps to Text Mining in Biomedical Literature.
• Oleg Okun: Protein Fold Recognition with K-Local Hyperplane Distance Nearest Neighbor Algorithm.
• F. Psomopoulos, S. Diplaris, and P. A. Mitkas: A Finite State Automata Based Technique for Protein Classification Rules Induction.
• Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, James Dowdall, Christos Andronis, Andreas Persidis, and Ourania Konstanti: Mining relations in the GENIA corpus.
  
  
  

Workshop Chair

Program Committee

• Sourav Bhomwick , Nanyang Technological University, Singapore.
  
• Christian Blaschke , Centro Nacional de Biotecnologia.
  
• Vladimir Brusic , Institute for Infocomm Research, Singapore.
  
• Carol Friedman, Columbia University.
  
• George Forman , Hewlett Packard.
  
• Rob Gaizauskas, University of Sheffield.
  
• Jörg Hakenberg, Humboldt-Universität zu Berlin.
  
• Ross King , University of Wales, Aberystwyth, and PharmaDM.
  
• Adam Kowalczyk, Telstra & Peter MacCallum Cancer Centre.
  
• Stefan Kramer , Technische Universität München.
  
• Ulf Leser, Humboldt-Universität zu Berlin.
  
• Bhavani Raskutti, Telstra.
  
• Steffen Schulze-Kremer , Max-Planck-Institute, Berlin.
  
• Myra Spiliopoulou , University of Magdeburg.
  
• Alfonso Valencia , Centro Nacional de Biotecnologia, Spain.
  
• David Vogel, AI Insight.
  
• Mohammed Zaki, Rensselaer Polytechnic Institute.
  

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