Bioinformatics Advance Access originally published online on August 12, 2004
Bioinformatics 2005 21(1):104-115; doi:10.1093/bioinformatics/bth464
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bioinformatics vol. 21 issue 1 © Oxford University Press 2005; all rights reserved.
Gene clustering by Latent Semantic Indexing of MEDLINE abstracts
1 Department of Neurology, University of Tennessee Health Science Center 855 Monroe Avenue, 416 Link Bldg, Memphis, TN 38163, USA
2 Department of Computer Science, University of Tennessee Knoxville, TN 37996-3450, USA
*To whom correspondence should be addressed.
Motivation: 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.
Availability: The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html
Contact: rhomayouni{at}utmem.edu
Supplementary information: http://shad.cs.utk.edu/sgo/pubs.html
Received on September 30, 2003; revised on July 10, 2004; accepted on August 2, 2004
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  Y. Lin, W. Li, K. Chen, and Y. Liu A Document Clustering and Ranking System for Exploring MEDLINE Citations J. Am. Med. Inform. Assoc., September 1, 2007; 14(5): 651 - 661. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 V. I. Torvik and N. R. Smalheiser A quantitative model for linking two disparate sets of articles in MEDLINE Bioinformatics, July 1, 2007; 23(13): 1658 - 1665. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Matsunaga and M.-a. Muramatsu Knowledge-based computational search for genes associated with the metabolic syndrome Bioinformatics, July 15, 2005; 21(14): 3146 - 3154. [Abstract] [Full Text] [PDF]
|
|