Interpreting patterns and analysis of acute leukemia gene expression data by multivariate fuzzy statistical analysis

Computers and Chemical Engineering

Volumn 29, Issue 6 SPEC. ISS., 2005, Pages 1345-1356

  Yoo, ChangKyoo ^a,b   Lee, In Beum ^b   Vanrolleghem, Peter A ^a  

^a GHENT UNIVERSITY   (Belgium)

^b Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Bioinformatics; Fuzzy clustering; Gene expression analysis; Gene selection; Leukemia gene expression; Molecular biology; Partial least squares (PLS)

Indexed keywords

COMPUTATIONAL METHODS; COST ACCOUNTING; DNA; FUZZY SETS; STATISTICAL METHODS; TUMORS;

DNA MICROARRAY TECHNOLOGIES; GENE EXPRESSION; LEUKEMIA; MULTIVARIATE FUZZY STATISTICAL ANALYSIS;

GENES;

BIOTECHNOLOGY;

EID: 18244393755     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2005.02.031     Document Type: Conference Paper

References (23)

1. A. Alizadeh, M.B. Eisen, and L.M. Staudt Different types of diffuse large b-cell lymphoma identified by gene expression profiling Nature 403 2000 503 511
2. U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, and A.J. Levine Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America 96 1999 6745 6750
3. S. Bicciato, M. Pandin, G. Didone, and C. Di Bello Pattern identification and classification in gene expression data using an autoassociative neural network model Biotechnology and Bioengineering 81 2002 594 606
4. J.-H. Cho, D.K. Lee, J.H. Park, K.W. Kim, and I.-B Lee Optimal approaches for classification of acute leukemia subtypes based on gene expression data Biotechnology Progress 18 4 2002 847 854
5. J. Dorrie, H. Gerauer, Y. Wachter, and S.J. Zunino Resveratrol induces extensive apoptosis by depolarizing mitochondrial membranes and activating caspase-9 in acute lymphoblastic leukemia cells Cancer Research 61 2001 4731 4739
6. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification (2nd ed.). New York: John Wiley & Sons.
7. S. Dudoit, J. Fridlyand, and T. Speed Comparison of discrimination methods for the classification of tumor using gene expression data Journal of American State Association 97 2002 77 87
8. T.R. Golub, D.K. Slonim, P. Tamayo, and E.S. Lander Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring Science 286 1999 531 537
9. P. Kebriaei, J. Anastasi, and R.A. Larson Acute lymphoblastic leukaemia: diagnosis and classification Best Practice and Research. Clinical Haematology 15 2002 597 621
10. Y. Lu, and J. Han Cancer classification using gene expression data Information Systems 28 2003 243 268
11. J. Lyons-Weiler, S. Patel, and S. Bhattacharya A classification-based machine learning approach for the analysis of genome-wide expression data Genome Research 13 2003 503 512
12. D.V. Nguyen, and D.M. Rocke Tumor classification by partial least squares using microarray gene expression Bioinformatics 18 1 2002 39 50
13. M.F. Ochs, and A.K. Godwin Microarrays in cancer: Research and applications BioTechniques 34 2003 S4 S15
14. P.J. Park, L. Tian, and I.S. Kohane Linking gene expression data with patient survival times using partial least squares Bioinformatics 18 1 2002 S120 S127
15. T. Ross Systematic variation in gene expression patterns in human cancer cell lines Nature Genetics 24 2000 227 234
16. U. Scherf A gene expression database for the molecular pharmacology of cancer Nature Genetics 24 2000 236 244
17. G. Stephanopoulos, D.H. Hwang, W.A. Schmit, J. Misra, and G. Stephanopoulos Mapping physiological states from microarray expression measurements Bioinformatics 18 8 2002 1054 1063
18. J. Stephenson Human genome studies expected to revolutionize cancer classification Journal of American Medical Association 282 1999 927 992
19. J.G. Thomas, J.M. Olson, S.J. Tapscott, and L.P. Zhao An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles Genome Research 11 2001 1227 1236
20. Y.H. Yang, S. Dudoit, P. Luu, D.M. Lin, V. Peng, J. Ngai, and T.P. Speed Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation Nucleic Acids Research 30 2002 15 21
21. E. Yeoh Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling Cancer Cell 1 2002 133 143
22. K.Y. Yeung, and W.L. Ruzzo Principal component analysis for clustering gene expression Bioinformatics 17 2001 763 774
23. C.K. Yoo, P.A. Vanrolleghem, and I. Lee Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical method in biological wastewater treatment plant Journal of Biotechnology 105 1-2 2003 135 161