WG: European Workshop on Data Mining and Text Mining for Bioinformatics: Final Call for Papers
codrina.lauth at ais.fraunhofer.de
Wed Jun 9 03:11:42 EST 2004
Data Mining and Text Mining for Bioinformatics
Second European Workshop at the ECML / PKDD 2004 in Pisa, Italy.
24. September, 2004.
Call for Papers
(Submission Deadline: June 14)
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
Goals and Intended Audience
In order to build knowledge discovery systems that contribute to
our understanding of biological systems, solutions to the above
problems have to be assembled into efficient and scalable
systems. The workshop aims at facilitating this process, and at
enhancing the exchange of knowledge between computational
biologists and knowledge discovery researchers. Accordingly, our
intended audience are both computational biologists and KDD
Topics of Interest
Topics include, but are not limited to:
* Application and assessment of data mining algorithms,
* Application and assessment of text mining algorithms,
* Information extraction from scientific papers,
* KD dealing with large and distributed data sets,
* Optimal database support for KD,
* Improving and assessing data quality in KD,
* Case studies,
* KD algorithms for drug de-velopment,
* Information integration for KD,
* Mining multi-relational data.
* Papers should deal with bio-medical data sets.
The Program Committee invites
* Research papers with unpublished results which may be up to
eight pages long using 10pt fonts,
* Research notes describing work in progress which may be up to
five pages using 10pt fonts, and
* Project descriptions, providing a description of the goals and
methods of research projects or PhD projects, up to five pages.
There are no format requirements. Submissions can be uploaded via
the workshop web site
submission deadline is June 14, 2004.
Workshop proceedings will be published on the web site and will
be available in printed form at the workshop.
* Submission Deadline: June 14, 2004.
* Acceptance Notification: July 5, 2004.
* Camera-ready Copy due: July 12, 2004.
* Workshop: September 24, 2004.
Tobias Scheffer, Humboldt-Universitaet zu Berlin.
* Sourav Bhomwick , Nanyang Technological University.
* 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 McCallum 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.
More information about the Biomatrx