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Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2756
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| Title: | Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks |
| Authors: | Hu, Xiaohua
Li, Guangren
Yoo, Illhoi
Zhang, Xiaodan
Hu, Xuheng |
| Issue Date: | 2007 |
| Publisher: | BioMed Central |
| Citation: | BMC Bioinformatics 2007, 8:324 |
| Abstract: | The problem of mining undiscovered public
knowledge from biomedical literature was exemplified by
Swanson’s pioneering work on Raynaud disease/fish-oil
discovery in 1986. Since then, there have been many approaches
to mine undiscovered public knowledge from biomedical
literature. This paper presents a semantic-based approach for
mining undiscovered public knowledge from bio-medical
literature. The method takes advantages of the biomedical
ontologies, MeSH and UMLS, as the source of semantic
knowledge. A prototype system Biomedical Semantic-based
Knowledge Discovery System (Bio-SbKDS) is designed to
uncover novel hypothe-sis/connections hidden in the biomedical
literature. Using the semantic types and semantic relations of the
bio-medical concepts, Bio-SbKDS can identify the relevant
concepts collected from Medline and generate the novel
hypothesis between these concepts. Bio-SbKDS suc-cessfully
replicates Dr. Swanson’s two famous discover-ies: Raynaud
disease/fish oil and migraine/magnesium. Compared with
previous approaches, our method searches much less articles,
generates much less but more relevant novel hypotheses, requires
much less human in-tervention in the discovery procedure. |
| URI: | http://dx.doi.org/10.1186/1471-2105-8-324
http://hdl.handle.net/1860/2756 |
| Appears in Collections: | Faculty Research and Publications (IST)
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