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<Paper uid="P04-1025">
  <Title>Extracting Regulatory Gene Expression Networks from PubMed</Title>
  <Section position="5" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have developed a method that allows us to extract information on regulation of gene expression from biomedical abstracts. This is a highly relevant biological problem, since much is known about it although this knowledge has yet to be collected in a database. Also, knowledge on how gene expression is regulated is crucial for interpreting the enormous amounts of gene expression data produced by high-throughput methods like spotted microarrays and GeneChips.</Paragraph>
    <Paragraph position="1"> Although we developed and evaluated our method on abstracts related to baker's yeast only, we have successfully applied the method to other organisms including humans (to be published elsewhere). The main adaptation required was to replace the list of synonymous gene/protein names to reflect the change of organism. Furthermore, we also intend to reuse the recognition of named entities to extract other, specific types of interactions between biological entities.</Paragraph>
  </Section>
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