Gene tree labeling using nonnegative matrix factorization on biomedical literature.

Gene tree labeling using nonnegative matrix factorization on biomedical literature. Research Abstract Details 

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Gene tree labeling using nonnegative matrix factorization on biomedical literature. Abstract Text:

Identifying functional groups of genes is a challenging problem for biological applications. Text mining approaches can be used to build hierarchical clusters or trees from the information in the biological literature. In particular, the nonnegative matrix factorization (NMF) is examined as one approach to label hierarchical trees. A generic labeling algorithm as well as an evaluation technique is proposed, and the effects of different NMF parameters with regard to convergence and labeling accuracy are discussed. The primary goals of this study are to provide a qualitative assessment of the NMF and its various parameters and initialization, to provide an automated way to classify biomedical data, and to provide a method for evaluating labeled data assuming a static input tree. As a byproduct, a method for generating gold standard trees is proposed.

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Gene tree labeling using nonnegative matrix factorization on biomedical literature. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Computational intelligence and neuroscience

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Page Numbers: 276535

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ISSN: 1687-5265

DAY: 23

MONTH: 04

YEAR: 2008

Gene tree labeling using nonnegative matrix factorization on biomedical literature. Information

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LANGUAGE: eng

NlmUniqueID: 101279357

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Grant and Affiliation Information for Gene tree labeling using nonnegative matrix factorization on biomedical literature.

AFFILIATION: Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996-3450, USA.

Country: United States

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MEDLINETA: Comput Intell Neurosci

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