The next generation of literature analysis: Integration of genomic analysis into text mining
Joined Genomatix Software GmbH in 2000, where he is Head of Discovery. He did his postdoctoral work in the group of Dr Werner at the GSF where he developed the first specific approach for genome wide promoter prediction in mammalian genomes. He has over 15 years of experience in pattern recognition, artificial intelligence and medicine.
Received a Masters Degree in Biology from LMU Munich and completed postgraduate studies in Computer Science at the Technische Universität München in 2001. His research interests include the design of software for natural language processing, and in particular information extraction techniques for systems biology. He is currently a scientist at Genomatix GmbH.
CEO and CSO of Genomatix Software GmbH. Since 1998 he has been a full-time bioinformatics researcher at the GSF-National Research Centre for Environment and Health in Neuherberg, Germany, focusing on the analysis of genomic sequences with special emphasis on aspect of the regulation of transcription. He founded Genomatix Software GmbH in 1997 and it has rapidly developed a unique expertise and advanced software for genomic research.
Matthias Scherf, Genomatix Software GmbH, Landsberger Strasse 6, Munich, D-80339, Germany Tel: +49 89 5997660 Fax:+49 89 59976655 E-mail: scherf{at}genomatix.de
Text-mining systems are indispensable tools to reduce the increasing flux of information in scientific literature to topics pertinent to a particular interest in focus. Most of the scientific literature is published as unstructured free text, complicating the development of data processing tools, which rely on structured information. To overcome the problems of free text analysis, structured, hand-curated information derived from literature is integrated in text-mining systems to improve precision and recall. In this paper several text-mining approaches are reviewed and the next step in development of text-mining systems, which is based on a concept of multiple lines of evidence, is described: results from literature analysis are combined with evidence from experiments and genome analysis to improve the accuracy of results and to generate additional knowledge beyond what is known solely from literature.
Keywords: literature/text mining, gene regulation, promoter analysis, integrated analysis
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