Publication: bioNMF: a versatile tool for non-negative matrix factorization in biology
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2006-07-28
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Biomed Central LTD
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Abstract
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.
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© 2006 Pascual-Montano et a.
This work has been partially funded by the Spanish grants CICYT BFU2004-00217/BMC, GEN2003-20235-c05-05, CYTED-505PI0058, TIN2005-5619, PR27/05-13964-BSCH and a collaborative grant between the Spanish CSIC and the Canadian NRC (CSIC-050402040003). PCS is recipient of a grant from CAM. APM acknowledges the support of the Spanish Ramón y Cajal program.
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1. Wall ME, Dyck PA, Brettin TS: SVDMAN – singular value decomposition analysis of microarray data. Bioinformatics 2001, 17:566-8.
2. Lee SI, Batzoglou S: Application of independent component analysis to microarrays. Genome Biol 2003, 4:R76.
3. Dai JJ, Lieu L, Rocke D: Dimension reduction for classification with gene expression microarray data. Stat Appl Genet Mol Biol 2006, 5:Article6.
4. Jansen JJ, Hoefsloot HC, Boelens HF, van der Greef J, Smilde AK: Analysis of longitudinal metabolomics data. Bioinformatics 2004, 20:2438-46.
5. Scholz M, Gatzek S, Sterling A, Fiehn O, Selbig J: Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 2004, 20:2447-54.
6. Lee KR, Lin X, Park DC, Eslava S: Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method. Proteomics 2003, 3:1680-6.
7. Lee DD, Seung HS: Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401:788-91.
8. Girolami M, Breitling R: Biologically valid linear factor models of gene expression. Bioinformatics 2004, 20:3021-33.
9. Brunet JP, Tamayo P, Golub TR, Mesirov JP: Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA 2004, 101:4164-9.
10. Carmona-Saez P, Pascual-Marqui RD, Tirado F, Carazo JM, Pascual- Montano A: Biclustering of gene expression data by nonsmooth non-negative matrix factorization. BMC Bioinformatics 2006, 7:78.
11. Carrasco DR, Tonon G, Huang Y, Zhang Y, Sinha R, Feng B, Stewart JP, Zhan F, Khatry D, Protopopova M, et al.: High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell 2006, 9:313-25.
12. Wang G, Kossenkov AV, Ochs MF: LS-NMF: a modified non-negative matrix factorization algorithm utilizing uncertainty estimates. BMC Bioinformatics 2006, 7:175.
13. Kim PM, Tidor B: Subsystem identification through dimensionality reduction of large-scale gene expression data. Genome Res 2003, 13:1706-18.
14. Gao Y, Church G: Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics 2005, 21:3970-5.
15. Inamura K, Fujiwara T, Hoshida Y, Isagawa T, Jones MH, Virtanen C, Shimane M, Satoh Y, Okumura S, Nakagawa K, et al.: Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization. Oncogene 2005, 24:7105-13.
16. Heger A, Holm L: Sensitive pattern discovery with 'fuzzy' alignments of distantly related proteins. Bioinformatics 2003, 19(Suppl 1):i130-7.
17. Pehkonen P, Wong G, Toronen P: Theme discovery from gene lists for identification and viewing of multiple functional groups. BMC Bioinformatics 2005, 6:162.
18. Chagoyen M, Carmona-Saez P, Shatkay H, Carazo JM, Pascual-Montano A: Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics 2006, 7:41.
19. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP: GenePattern 2.0. Nat Genet 2006, 38:500-1.
20. Pascual-Montano A, Carazo JM, Kochi K, Lehmann D, Pascual-Marqui RD: Non-smooth Non-Negative Matrix Factorization (nsNMF). IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28:403-415.
21. Garcia de la Nava J, Santaella DF, Cuenca Alba J, Maria Carazo J, Trelles O, Pascual-Montano A: Engene: the processing and exploratory analysis of gene expression data. Bioinformatics 2003, 19:657-8.
22. Getz G, Levine E, Domany E: Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci USA 2000, 97:12079-84.
23. Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, et al.: Functional discovery via a compendium of expression profiles. Cell 2000, 102:109-26.
24. Madeira SC, Oliveira AL: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2004, 1:24-45.
25. Monti S, Tamayo P, Mesirov J, Golub T: Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning 2003, 52:91-118.
26. Lee DD, Seung HS: Algorithms for non-negative matrix factorization. Adv Neural Info Proc Syst 2001, 13:556-562.
27. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-7.