A multi-strategy approach to informative gene identification from gene expression data

Journal of Bioinformatics and Computational Biology

Volumn 8, Issue 1, 2010, Pages 19-38

  Liu, Ziying ^a   Phan, Sieu ^a   Famili, Fazel ^a   Pan, Youlian ^a   Lenferink, Anne E G ^a   Cantin, Christiane ^a   Collins, Catherine ^a   O'Connor Mccourt, Maureen D ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Data mining and knowledge discovery; Gene expression data analysis; Multi strategy learning

Indexed keywords

TRANSFORMING GROWTH FACTOR BETA;

ACUTE GRANULOCYTIC LEUKEMIA; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CELL TRANSFORMATION; DECISION TREE; DNA MICROARRAY; DRUG EFFECT; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; GENOMICS; HUMAN; MOUSE; STATISTICS;

ANIMALS; ARTIFICIAL INTELLIGENCE; CELL TRANSFORMATION, NEOPLASTIC; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; DECISION TREES; GENE EXPRESSION PROFILING; GENOMICS; HUMANS; LEUKEMIA, MYELOID, ACUTE; MICE; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; TRANSFORMING GROWTH FACTOR BETA;

EID: 77951606952     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720010004495     Document Type: Article

References (37)

1. Cuperlovic-Culf M, Belacel N, Ouellette R, Determination of tumour marker genes from gene expression data, Drug Discov Today 10(6):429-437, 2005.
2. Dietterich TG, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization, Mach Learn 40(2):1-19, 2000.
3. Bloedorn E, Michalski RS, Wnek J, Multistrategy constructive Induction: AQ17-MCI, in Proc. Second Int. Workshop on Multistrategy Learning (MSL93), Harpers Ferry, Morgan Kaufmann, pp. 188-203, 1993.
4. Hsu WH, Welge M, Redman T, Clutter D, High performance commercial data mining: A multi-strategy machine learning application, Data Min Knowl Discov 6(4):361-391, 2002.
5. Geurts P, Ernst D, Wehenkel L, Extremely randomized trees Source, Mach Learn 63(1):3-42, 2006.
6. Amershi S, Conati C, Unsupervised and supervised machine learning in user modeling for intelligent learning environments, in Proc. 12th Int Conf on Intelligent User Interfaces IUI '07, ACM, pp. 72-81, 2007.
7. Kamishima T, Motoyoshi F, Learning from cluster examples, Mach Learn 53(3):199-233, 2003.
8. Jeffery IB, Higgins DG, Culhane AC, Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data, BMC Bioinform 7:359, 2006.
9. Kadota K, Nakai J, Shimizu K, A weighted average difference method for detecting differentially expressed genes from microarray data, Algorithms Mol Biol 3:8, 2008.
10. Hua J, Tembe WD, Doughertya ER, Performance of feature-selection methods in the classification of high-dimension data, Pattern Recog 42(3):409-424, 2009.
11. Abruzzo LV, Wang J, Kapoor M, Medeiros LJ, Keating MJ, Highsmith WE, Barron LL, Cromwell CC, Coombes KR, Biological validation of differentially expressed genes in chronic lymphocytic leukemia identified by applying multiple statistical methods to oligonucleotide microarrays, J Mol Diagn 7(3):337-345, 2005.
12. Diaz-Uriarte R, Alvarez de Andres S, Gene selection and classification of microarray data using random forest, BMC Bioinform 7:3, 2006.
13. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G, The gene ontology consortium: Gene ontology: Tool for the unification of biology, Nat Genet 25(1):25-29, 2000.
14. Kanehisa M, Goto S, KEGG: Kyoto Encyclopedia of genes and genomes, Nucleic Acids Res 28(1):27-30, 2000.
15. Lenferink AEG, Magoon J, Cantin C, O'Connor-McCourt MD, Investigation of three new mouse mammary tumor cell lines as models for transforming growth factor (TGF)-β and Neu pathway signaling studies: Identification of a novel model for TGF-β-induced epithelial-to-mesenchymal transition, Breast Cancer Res 6(5):514-530, 2004.
16. Stirewalt D, Meshinchi S, Kopecky KJ, Fan W, Pogosova-Agadjanyan EL, Engel JH, Cronk MR, Dorcy KS, McQuary MR, Hockenbery D, Wood D, Heimfeld S, Radich JP, Identification of genes with abnormal expression changes in acute myeloid leukemia, Genes Chromosomes Cancer 47(1):8-20, 2008.
17. Tusher VG, Tibshirani R, Chu G, Significance analysis of microarrays applied to the ionizing radiation response, PNAS 98(9):5116-5121, 2001.
18. Breitling R, Armengaud P, Amtmann A, Herzyk P, Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Lett 573(1-3):83-92, 2004.
19. Jr GD, Sherman BT, Hosack DA, Yang Y, Gao W, Lane HC, Lempicki RA, Software DAVID: Database for annotation, visualization, and integrated discovery, Genome Biol 4(5):R60, 2003.
20. Pan Y, Phan S, Threshold for positional weight matrix, Eng Lett 16(4):498-504, 2008.
21. Eddy SR, Profile hidden Markov models, Bioinformatics 14(9):755-763, 1998.
22. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E, TRANSFAC and its module TRANSCompel: Transcrip-tional gene regulation in eukaryotes, Nucleic Acids Res 34:108-110, 2006.
23. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP, Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, PNAS 102(43):15545-15550, 2005.
24. Hill JJ, Tremblay TL, Cantin C, O'Connor-McCourt MD, Kelly JF, Lenferink AEG, Glycoproteomic analysis of two mouse mammary cell lines during transforming growth factor (TGF)-β induced epithelial to mesenchymal transition, Proteome Sci 7:2, 2009.
25. Pontier SM, Muller WJ, Integrins in breast cancer dormancy, APMIS 116(7-8):677-684, 2008.
26. Perrone G, Altomare V, Zagami M, Morini S, Petitti T, Battista C, Muda AO, Rabitti C, Caveolin-1 expression in human breast lobular cancer progression, Mod Pathol 22(1):71-78, 2009.
27. Roussidis AE, Theocharis AD, Tzanakakis GN, Karamanos NK, The importance of c-kit and PDGF receptors as potential targets for molecular therapy in breast cancer, Curr Med Chem 14(7):735-743, 2007.
28. Bioinformatics Organization, Inc. http://www.bioinformatics.org/legend/ legend.htm. Accessed in March, 2009.
29. Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D, GeneCards: Integrating information about genes, proteins and diseases, Trends Genet 13(4):163, 1997.
30. The UniProt Consortium, The Universal Protein Resource (UniProt), Nucleic Acids Res 35 (Database issue) D193-D197, 2007.
31. Garber ME, Troyanskaya OG, Schluens K, et al. Diversity of gene expression in ade-nocarcinoma of the lung, Proc Natl Acad Sci USA 98:13784-13789, 2001.
32. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M et al., Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses, Proc Natl Acad Sci USA 98:13790-13795, 2001.
33. Haslett JN, Sanoudou D, Kho AT, Bennett RR, Greenberg SA, Kohane IS, Beggs AH, Kunkel LM, Gene expression comparison of biopsies from Duchenne muscular dystrophy (DMD) and normal skeletal muscle, Proc Natl Acad Sci USA 99:15000-15005, 2002.
34. Pescatori M, Broccolini A, Minetti C, Bertini E, Bruno C, D'Amico A, Bernardini C, Mirabella M, Silvestri G, Giglio V, Modoni A, Pedemonte M, Tasca G, Galluzzi G, Mercuri E, Tonali PA, Ricci E, Gene expression profiling in the early phases of DMD: A constant molecular signature characterizes DMD muscle from early postnatal life throughout disease progression, FASEB J 21:1210-1226, 2007.
35. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP, Summaries of Affymetrix GeneChip probe level data, Nucleic Acids Res 31(4):e15, 2003.
36. Zhang M, Zhang L, Zou JF, Yao C, Xiao H, Liu Q, Wang J, Wang D, Wang CG, Guo Z, Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes, Bioinformatics 25(13):1662-1668, 2009.
37. Bussey KJ, Kane D, Sunshine M, Narasimhan S, Nishizuka S, Reinhold WC, Zeeberg B, Ajay, Weinstein JN, MatchMiner: A tool for batch navigation among gene and gene product identifiers, Genome Biol 4(4):R27, 2003.