Copyright © 2008 Kevin E. Heinrich et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

1. K. E. Heinrich, Finding functional gene relationships using the semantic gene organizer (SGO), M.S. thesis, Department of Computer Science, University of Tennessee, Knoxville, Tenn, USA, 2004.
2. R. Homayouni, K. Heinrich, L. Wei, and M. W. Berry, “Gene clustering by latent semantic indexing of MEDLINE abstracts,” Bioinformatics, vol. 21, no. 1, pp. 104–115, 2005. View at Publisher · View at Google Scholar · View at PubMed
3. G. Golub and C. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, Md, USA, 3rd edition, 1996.
4. P. Paatero and U. Tapper, “Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values,” Environmetrics, vol. 5, no. 2, pp. 111–126, 1994. View at Publisher · View at Google Scholar
5. D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, 1999. View at Publisher · View at Google Scholar · View at PubMed
6. L. Weixiang, Z. Nanning, and Y. Qubo, “Nonnegative matrix factorization and its applications in pattern recognition,” Chinese Science Bulletin, vol. 51, no. 1, pp. 7–18, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
7. K. E. Heinrich, Automated gene classification using nonnegative matrix factorization on biomedical literature, Ph.D. thesis, Department of Computer Science, University of Tennessee, Knoxville, Tenn, USA, 2007.
8. M. W. Berry and M. Browne, Understanding Search Engines: Mathematical Modeling and Text Retrieval, SIAM, Philadelphia, Pa, USA, 1999.
9. S. Wild, J. Curry, and A. Dougherty, “Motivating non-negative matrix factorizations,” in Proceedings of the 8th SIAM Conference on Applied Linear Algebra (LA '03), Williamsburg, Va, USA, June, 2003.
10. D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Advances in Neural and Information Processing Systems, T. K. Leen, T. G. Dietterich, and V. Tresp, Eds., vol. 13, pp. 556–562, MIT Press, Cambridge, Mass, USA, 2001.
11. M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons, “Algorithms and applications for approximate nonnegative matrix factorization,” Computational Statistics and Data Analysis, vol. 52, no. 1, pp. 155–173, 2007. View at Publisher · View at Google Scholar
12. C. Eckart and G. Young, “The approximation of one matrix by another of lower rank,” Psychometrika, vol. 1, no. 3, pp. 211–218, 1936. View at Publisher · View at Google Scholar
13. C. Boutsidis and E. Gallopoulos, “On SVD-based initialization for nonnegative matrix factorization,” University of Patras, Patras, Greece, 2005.
14. R. Desper and O. Gascuel, “Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle,” Journal of Computational Biology, vol. 9, no. 5, pp. 687–705, 2002. View at Publisher · View at Google Scholar · View at PubMed
15. S. Kiritchenko, Hierarchical text categorization and its applications to bioinformatics, Ph.D. thesis, University of Ottawa, Ottawa, Canada, 2005.
16. M. Chagoyen, P. Carmona-Saez, H. Shatkay, J. M. Carazo, and A. Pascual-Montano, “Discovering semantic features in the literature: a foundation for building functional associations,” BMC Bioinformatics, vol. 7, article 41, pp. 1–19, 2006. View at Publisher · View at Google Scholar · View at PubMed
17. C. Boutsidis and E. Gallopoulos, “SVD based initialization: a head start for nonnegative matrix factorization,” University of Patras, Patras, Greece, 2007.
18. A. Langville, C. Meyer, and R. Albright, “Initializations for the nonnegative matrix factorization,” preprint, 2006.
19. F. Shahnaz, M. W. Berry, V. P. Pauca, and R. J. Plemmons, “Document clustering using nonnegative matrix factorization,” Information Processing & Management, vol. 42, no. 2, pp. 373–386, 2006. View at Publisher · View at Google Scholar
20. A. Pascual-Montano, J. M. Carazo, K. Kochi, D. Lehmann, and R.D. Pascual-Marqui, “Nonsmooth nonnegative matrix factorization (nsNMF),” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 403–415, 2006. View at Publisher · View at Google Scholar · View at PubMed
21. P. Carmona-Saez, R. D. Pascual-Marqui, F. Tirado, J. M. Carazo, and A. Pascual-Montano, “Biclustering of gene expression data by non-smooth nonnegative matrix factorization,” BMC Bioinformatics, vol. 7, article 78, pp. 1–18, 2006. View at Publisher · View at Google Scholar · View at PubMed
22. C. Ding, T. Li, W. Peng, and H. Park, “Orthogonal nonnegative matrix tri-factorizations for clustering,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 126–135, ACM Press, Philadelphia, Pa, USA, August 2006.
23. J.-P. Brunet, P. Tamayo, T. R. Golub, and J. P. Mesirov, “Metagenes and molecular pattern discovery using matrix factorization,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 12, pp. 4164–4169, 2004. View at Publisher · View at Google Scholar · View at PubMed
24. A. Cichocki, R. Zdunek, S. Choi, R. Plemmons, and S.-I. Amari, “Novel multi-layer nonnegative tensor factorization with sparsity constraints,” in Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'07), vol. 4432 of Lecture Notes in Computer Science, pp. 271–280, Warsaw, Poland, April 2007. View at Publisher · View at Google Scholar
25. M. Ashburner, C. A. Ball, J. A. Blake, et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at PubMed