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Altman paper of interest

Mike Cherry cherry at genome.Stanford.EDU
Sat Jan 19 19:59:02 PST 2002

Here is a paper that you might find of interest.  They use natural
language processing to association GO terms with gene products.

Raychaudhuri, Chang, Sutphin and Altman (2202) Associating Genes with
Gene Ontology Codes Using a Maximum Entropy Analysis of Biomedical
Literature.  Genome Res.  12:203-214


Functional characterizations of thousands of gene products from many
species are described in the published literature. These discussions
are extremely valuable for characterizing the functions not only of
these gene products, but also of their homologs in other
organisms. The Gene Ontology (GO) is an effort to create a controlled
terminology for labeling gene functions in a more precise, reliable,
computer-readable manner. Currently, the best annotations of gene
function with the GO are performed by highly trained biologists who
read the literature and select appropriate codes. In this study, we
explored the possibility that statistical natural language processing
techniques can be used to assign GO codes. We compared three document
classification methods (maximum entropy modeling, nae Bayes
classification, and nearest-neighbor classification) to the problem of
associating a set of GO codes (for biological process) to literature
abstracts and thus to the genes associated with the abstracts. We
showed that maximum entropy modeling outperforms the other methods and
achieves an accuracy of 72% when ascertaining the function discussed
within an abstract. The maximum entropy method provides confidence
measures that correlate well with performance. We conclude that
statistical methods may be used to assign GO codes and may be useful
for the difficult task of reassignment as terminology standards evolve
over time.

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