Extracting unrecognized gene relationships from the biomedical literature via matrix factorizations using a priori knowledge of gene relationships

Hyunsoo Kim, Haesun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The construction of literature-based networks of gene-gene interactions is one of the most important applications of text mining in bioinformatics. Extracting potential gene relationships from the biomedical literature may be helpful in building biological hypotheses that can be explored further experimentally. In this paper, we explore the utility of singular value decomposition (SVD) and non-negative matrix factorization (NMF) to extract unrecognized gene relationships from the biomedical literature by taking advantage of known gene relationships. We introduce a way to incorporate a priori knowledge of gene relationships into LSI/SVD and NMF. In addition, we propose a gene retrieval method based on NMF (GR/NMF), which shows comparable performance with latent semantic indexing based on SVD.

Original languageEnglish (US)
Title of host publicationCIKM 2006 Workshop - Proceedings of TMBIO 2006
Subtitle of host publicationACM First International Workshop on Text Mining in Bioinformatics
Pages60-67
Number of pages8
DOIs
StatePublished - 2006
Externally publishedYes
EventTMBIO 2006: ACM 1st International Workshop on Text Mining in Bioinformatics, held in conjunction with the ACM 15th Conference on Information and Knowledge Management, CIKM 2006 - Arlington, VA, United States
Duration: Nov 10 2006Nov 10 2006

Publication series

NameProceedings of TMBIO 2006: ACM First International Workshop on Text Mining in Bioinformatics

Conference

ConferenceTMBIO 2006: ACM 1st International Workshop on Text Mining in Bioinformatics, held in conjunction with the ACM 15th Conference on Information and Knowledge Management, CIKM 2006
Country/TerritoryUnited States
CityArlington, VA
Period11/10/0611/10/06

Keywords

  • Gene relationships
  • Non-negative matrix factorization
  • Singular value decomposition

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