Gene clustering by latent semantic indexing of MEDLINE abstracts.
BACKGROUND
A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations.


RESULTS
We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments.


BACKGROUND
The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html.
Department of Neurology, University of Tennessee Health Science Center, 855 Monroe Avenue, 416 Link Bldg, Memphis, TN 38163, USA. rhomayouni@utmem.edu
Ramin
R
Homayouni
Kevin
K
Heinrich
Lai
L
Wei
Michael W
MW
Berry
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D016000:Cluster Analysis; D004282:Documentation / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000145:classification* / Q000378:metabolism*; D012660:Semantics; D015398:Signal Transduction / Q000502:physiology
D011506:Proteins