Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model |
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Authors: | Xin He Moushumi Sen Sarma Xu Ling Brant Chee Chengxiang Zhai Bruce Schatz |
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Institution: | (1) Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;(2) Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA |
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Abstract: | Background Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns.
The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This
analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO).
However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some
important biological domains inadequately covered. |
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Keywords: | |
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