title: Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts.
abstract: BACKGROUND
  The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs.
  
  
  RESULTS
  For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms.
  
  
  CONCLUSIONS
  LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin .
authors: Bioinformatics Program, University of Memphis, Memphis, 38152, USA.|Center for Translational Informatics, University of Memphis, Memphis, 38152, USA.
authors: Sujoy
authors: S
authors: Roy
authors: Bioinformatics Program, University of Memphis, Memphis, 38152, USA.
authors: Brandon C
authors: BC
authors: Curry
authors: Department of Mathematical Sciences, University of Memphis, Memphis, 38152, USA.
authors: Behrouz
authors: B
authors: Madahian
authors: Bioinformatics Program, University of Memphis, Memphis, 38152, USA. rhomayon@memphis.edu.|Center for Translational Informatics, University of Memphis, Memphis, 38152, USA. rhomayon@memphis.edu.|Department of Biology, University of Memphis, Memphis, 38152, USA. rhomayon@memphis.edu.
authors: Ramin
authors: R
authors: Homayouni
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D006801:Humans; D016239:MEDLINE; D035683:MicroRNAs; D058977:Molecular Sequence Annotation / Q000379:methods*; D012660:Semantics; D012984:Software
chemical_list: D035683:MicroRNAs
keywords: Latent semantic indexing; MicroRNAs; Singular value decomposition; Text mining