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dc.contributor.authorKim, Hyunsoo
dc.contributor.authorPark, Haesun
dc.date.accessioned2007-05-23T22:19:58Z
dc.date.available2007-05-23T22:19:58Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/1853/14444
dc.description.abstractThe 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 nonnegative 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.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesCSE Technical Reports; GT-CSE-06-17en
dc.subjectGene relationshipsen
dc.subjectNon-negative matrix factorizationen
dc.subjectSingular value decompositionen
dc.titleExtracting Unrecognized Gene Relationships From the Biomedical Literature via Matrix Factorizations Using a Priori Knowledge of Gene Relationshipsen
dc.typeTechnical Reporten


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