bioNMF: a web-based tool for nonnegative matrix factorization in biology

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Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.
© 2008 The Author(s). This work has been partially funded by the Spanish grants BIO2007-67150-C03-02, S-Gen-0166/2006, CYTED-505 PI0058, CSD00C-07-20811 and TIN2005-5619. E.M.R. is supported by the grant FPU from the Spanish Ministry of Education. A.P.M. acknowledges the support of the Spanish Ramón y Cajal program. Funding to pay the Open Access publication charges for this article was provided by Spanish Grant. BIO2007-67150-C03-02.
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1. Lee,D.D. and Seung,H.S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791. 2. Brunet,J.P., Tamayo,P., Golub,T.R. and Mesirov,J.P. (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA, 101, 4164–4169. 3. Carmona-Saez,P., Pascual-Marqui,R.D., Tirado,F., Carazo,J.M. and Pascual-Montano,A. (2006) Biclustering of gene expression data by non-smooth non-negative matrix factorization. BMC Bioinform., 7, 78. 4. Tamayo,P., Scanfeld,D., Ebert,B.L., Gillette,M.A., Roberts,C.W. and Mesirov,J.P. (2007) Metagene projection for cross-platform, cross-species characterization of global transcriptional states. Proc. Natl Acad. Sci. USA, 104, 5959–5964. 5. Inamura,K., Fujiwara,T., Hoshida,Y., Isagawa,T., Jones,M.H., Virtanen,C., Shimane,M., Satoh,Y., Okumura,S., Nakagawa,K. et al. (2005) Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization. Oncogene, 24, 7105–7113. 6. Chagoyen,M., Carmona-Saez,P., Shatkay,H., Carazo,J.M. and Pascual-Montano,A. (2006) Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinform., 7, 41. 7. Pehkonen,P., Wong,G. and Toronen,P. (2005) Theme discovery from gene lists for identification and viewing of multiple functional groups. BMC Bioinform., 6, 162. 8. Dueck,D., Morris,Q.D. and Frey,B.J. (2005) Multi-way clustering of microarray data using probabilistic sparse matrix factorization. Bioinformatics, 21 (Suppl. 1), i144–i151. 9. Venter,J.C., Adams,M.D., Myers,E.W., Li,P.W., Mural,R.J., Sutton,G.G., Smith,H.O., Yandell,M., Evans,C.A., Holt,R.A. et al. (2001) The sequence of the human genome. Science, 291, 1304–1351. 10. Heger,A. and Holm,L. (2003) Sensitive pattern discovery with ‘fuzzy’ alignments of distantly related proteins. Bioinformatics, 19 (Suppl. 1), i130–i137. 11. Lohmann,G., Volz,K.G. and Ullsperger,M. (2007) Using nonnegative matrix factorization for single-trial analysis of fMRI data. Neuroimage, 37, 1148–1160. 12. Pascual-Montano,A., Carmona-Saez,P., Chagoyen,M., Tirado,F., Carazo,J.M. and Pascual-Marqui,R.D. (2006) bioNMF: a versatile tool for non-negative matrix factorization in biology. BMC Bioinform., 7, 366. 13. Kim,P.M. and Tidor,B. (2003) Subsystem identification through dimensionality reduction of large-scale gene expression data. Genome Res., 13, 1706–1718. 14. Pascual-Montano,A., Carazo,J.M., Kochi,K., Lehmann,D. and Pascual-Marqui,R.D. (2006) Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Trans. Pattern Anal. Mach. Intell., 28, 403–415. 15. Getz,G., Levine,E. and Domany,E. (2000) Coupled two-way clustering analysis of gene microarray data. Proc. Natl Acad. Sci. USA, 97, 12079–12084. 16. Nielsen,T.O., West,R.B., Linn,S.C., Alter,O., Knowling,M.A., O’Connell,J.X., Zhu,S., Fero,M., Sherlock,G., Pollack,J.R. et al. (2002) Molecular characterisation of soft tissue tumours: a gene expression study. Lancet, 359, 1301–1307. 17. Golub,T.R., Slonim,D.K., Tamayo,P., Huard,C., Gaasenbeek,M., Mesirov,J.P., Coller,H., Loh,M.L., Downing,J.R., Caligiuri,M.A. et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531–537. 18. Cichocki,A., Zdunek,R. and Amari,S. (2008) Nonnegative matrix and tensor factorization. IEEE Signal Processing Magazine, 25, 142–145. 19. Cichocki,A. and Zdunek,R. (2007) Regularized alternating least squares algorithms for non-negative matrix/tensor factorizations. Lect. Notes Comput. Sci., 4493, 793–802. 20. Cichocki,A., Zdunek,R. and Amari,S. (2007) Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization. Lect. Notes Comput. Sci., 4666, 169–176.