Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery

BMC Bioinformatics

Volumn 12, Issue SUPPL. 1, 2011, Pages

  Han, Henry ^a,b   Li, Xiao Li ^c  

^a UNIVERSITY OF MICHIGAN   (United States)

^b EASTERN MICHIGAN UNIVERSITY   (United States)

^c INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; EXPERIMENTAL COMPARISON; FEATURE SELECTION METHODS; INDEPENDENT COMPONENT ANALYSIS(ICA); LINEAR DISCRIMINANT ANALYSIS; LOCAL FEATURE EXTRACTION; NONNEGATIVE MATRIX FACTORIZATION; STATE-OF-THE-ART ALGORITHMS;

ALGORITHMS; CLASSIFIERS; DIAGNOSIS; DISEASES; FEATURE EXTRACTION; GENE EXPRESSION; INDEPENDENT COMPONENT ANALYSIS; MICA; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES; TUMORS; BIOINFORMATICS;

DATA MINING;

TUMOR MARKER;

ALGORITHM; ARTICLE; CLASSIFICATION; COMPARATIVE STUDY; COMPUTER PROGRAM; DISCRIMINANT ANALYSIS; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETICS; METHODOLOGY; NEOPLASM; PRINCIPAL COMPONENT ANALYSIS; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; DISCRIMINANT ANALYSIS; GENE EXPRESSION PROFILING; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SENSITIVITY AND SPECIFICITY; SOFTWARE; TUMOR MARKERS, BIOLOGICAL;

EID: 79951547829     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-S1-S7     Document Type: Article

References (24)

1. Wang Y, Klijn J, Zhang, Atkins, Foeken J. Gene expression profiles and prognostic markers for primary breast cancer. Methods Mol Biol 2007, 377:131-138. full_text, 17634613.
2. Zhou X, Tuc D. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics 2007, 23(9):1106-1114. 10.1093/bioinformatics/btm036, 17494773.
3. Jolliffe I. Principal component analysis 2002, Springer Series in Statistics, 2nd ed., Springer, New York.
4. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 1999, 10(3):626-634.
5. Lee D, Seung H. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401:788-791. 10.1038/44565, 10548103.
6. Brunet J, Tamayo P, Golub T, Mesirov J. Molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A 2004, 101(12):4164-4169. 10.1073/pnas.0308531101, 384712, 15016911.
7. Gao Y, Church G. Improving molecular cancer class discovery through sparse nonnegative matrix factorization. Bioinformatics 2005, 21(21):3970-3975. 10.1093/bioinformatics/bti653, 16244221.
8. Han X. Nonnegative Principal component Analysis for Cancer Molecular Pattern Discovery. IEEE/ACM Trans Comput Biol Bioinform 2010, 7(3):537-549. 10.1109/TCBB.2009.36, 20671323.
9. Mallat S. A wavelet tour of signal processing 1999, Acad. Press, San Diego.
10. Vapnik V. Statistical Learning Theory 1998, John Wiley & Son, Inc., New York.
11. Boersma BJ, Reimers M, Yi M, Ludwig J, et al. A stromal gene signature associated with inflammatory breast cancer. Int J Cancer 2008, 15(122(6)):1324-1332.
12. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 25(365(9460)):671-679.
13. Singh D, Febbo P, Ross K, Jackson D, Manola J, Ladd C, et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1(2):203-209. 10.1016/S1535-6108(02)00030-2, 12086878.
14. Nutt CL, Mani D, Betensky R, Tamayo P, Cairncross J, et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research 2003, 63(7):1602-1607.
15. Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, et al. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. Lancet 2003, 361:923-929. 10.1016/S0140-6736(03)12775-4, 12648972.
16. van't Veer L, Dai H, Van De Vijver M, He Y, et al. Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer. Nature 2002, 415:530-536.
17. Martinez A, Kak A. PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 23(2):228-233.
18. Holtkamp N, Ziegenhagen N, Malzer E, Hartman C, Giese A, et al. Characterization of the amplicon on chromosomal segment 4q12 in glioblastoma multiforme. Neuro Oncol 2007, 9(3):291-297. 10.1215/15228517-2007-009, 1907422, 17504929.
19. Milde-Langosch K, Janke S, Wagner I, Schroder C, Streichert T, et al. Role of Fra-2 in breast cancer: influence on tumor cell invasion and motility. Breast Cancer Res Treat 2008, 107(3):337-47. 10.1007/s10549-007-9559-y, 17393299.
20. Langer S, Singer CF, Hudelist G, Dampier B, Kaserer K, et al. Jun and Fos family protein expression in human breast cancer: correlation of protein expression and clinicopathological parameters. Eur J Gynaecol Oncol 2006, 27(4):345-52.
21. Yu K, Lee C, Tan PH, Tan P. Conservation of Breast Cancer Molecular Subtypes and Transcriptional Patterns of Tumor Progression Across Distinct Ethnic Populations. Clinical Cancer Research 2004, 10:5508-5517. 10.1158/1078-0432.CCR-04-0085, 15328190.
22. Lacroix M, Toillon R, Leclercq G. p53 and breast cancer, an update. Endocrine-Related Cancer 2006, 13(2):293-325. 10.1677/erc.1.01172, 16728565.
23. Bach F, Jordan M. Kernel independent component analysis. Journal of Machine Learning and Research 2002, 3:1-48.
24. Schölkopf B, Smola A, Müller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998, 10:1299-1319.