Identification and optimization of classifier genes from multi-class earthworm microarray dataset

PLoS ONE

Volumn 5, Issue 10, 2010, Pages

  Li, Ying ^a   Wang, Nan ^a   Perkins, Edward J ^b   Zhang, Chaoyang ^a   Gong, Ping ^b  

^a UNIVERSITY OF SOUTHERN MISSISSIPPI   (United States)

^b U S ARMY ENGINEER RESEARCH AND DEVELOPMENT CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; CYCLONITE; TRINITROTOLUENE;

ACCURACY; ARTICLE; CLASSIFIER; CONTROLLED STUDY; DECISION TREE; EARTHWORM; ECOTOXICITY; GENE CLUSTER; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; GENETIC ALGORITHM; MICROARRAY ANALYSIS; NONHUMAN; PREDICTION; PROCESS MODEL; PROCESS OPTIMIZATION; RISK ASSESSMENT; ALGORITHM; ANIMAL; ANNELID WORM; GENETICS;

ALGORITHMS; ANIMALS; GENE EXPRESSION PROFILING; OLIGOCHAETA;

EID: 78149437909     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0013715     Document Type: Article

References (48)

1. Antonov AV, Tetko IV, Mader MT, Budczies J, Mewes HW (2004) Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics 20: 644-652.
2. Chi JT, Rodriguez EH, Wang Z, Nuyten DS, Mukherjee S, et al. (2007) Gene expression programs of human smooth muscle cells: tissue-specific differentiation and prognostic significance in breast cancers. PLoS Genet 3: 1770-1784.
3. Choi JK, Yu U, Yoo OJ, Kim S (2005) Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21: 4348-4355.
4. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-537.
5. Yang Y, Adelstein SJ, Kassis AI (2009) Target discovery from data mining approaches. Drug Discov Today 14: 147-154.
6. Huang LT (2009) An integrated method for cancer classification and rule extraction from microarray data. J Biomed Sci 16: 25.
7. Gong P, Guan X, Inouye LS, Pirooznia M, Indest KJ, et al. (2007) Toxicogenomic analysis provides new insights into molecular mechanisms of the sublethal toxicity of 2,4,6-trinitrotoluene in Eisenia fetida. Environ Sci Technol 41: 8195-8202.
8. Gong P, Guan X, Inouye LS, Deng Y, Pirooznia M, et al. (2008) Transcriptomic analysis of RDX and TNT interactive sublethal effects in the earthworm Eisenia fetida. BMC Genomics 9 Suppl 1: S15.
9. Kuperman RG, Simini M, Siciliano SD, Gong P (2009) Effects of energetic materials on soil organisms. In: Sunahara GI, Lotufo GR, Kuperman RG, Hawari J, eds. Ecotoxicology of Explosives. Boca RatonFL: CRC Press. pp35-76.
10. Falciani F, Diab AM, Sabine V, Williams TD, Ortega F, et al. (2008) Hepatic transcriptomic profiles of European flounder (Platichthys flesus) from field sites andcomputational approaches to predict site from stress gene responses following exposure to model toxicants. Aquat Toxicol 90: 92-101.
11. Nota B, Verweij RA, Molenaar D, Ylstra B, van Straalen NM, et al. (2010) Geneexpression analysis reveals a gene set discriminatory to different metals in soil. Toxicol Sci 115: 34-40.
12. Wang RL, Bencic D, Biales A, Lattier D, Kostich M, et al. (2008) DNA microarray-based ecotoxicological biomarker discovery in a small fish model species. Environ Toxicol Chem 27: 664-675.
13. Ankley GT, Daston GP, Degitz SJ, Denslow ND, Hoke RA, et al. (2006)Toxicogenomics in regulatory ecotoxicology. Environ Sci Technol 40:4055-4065.
14. Moens LN, van der V, Van RP, Del-Favero J, De Coen WM (2006) Expressionprofiling of endocrine-disrupting compounds using a customized Cyprinus carpio cDNA microarray. Toxicol Sci 93: 298-310.
15. Poynton HC, Zuzow R, Loguinov AV, Perkins EJ, Vulpe CD (2008) Gene expression profiling in Daphnia magna, part II: validation of a copper specific gene expression signature with effluent from two copper mines in California. Environ Sci Technol 42: 6257-6263.
16. Svendsen C, Owen J, Kille P, Wren J, Jonker MJ, et al. (2008) Comparative transcriptomic responses to chronic cadmium, fluoranthene, and atrazine exposure in Lumbricus rubellus. Environ Sci Technol 42: 4208-4214.
17. Gong P, Pirooznia M, Guan X, Perkins EJ (2010) Design, validation and annotation of transcriptome-wide oligonucleotide probes for the oligochaete annelid Eisenia fetida. PLoS One, Submitted.
18. Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207-210.
19. Powell WB (2007) Approximate Dynamic Programming: Solving the Curse of Dimensionality. Hoboken, NJ: Wiley.
20. Trevino V, Falciani F (2006) GALGO: an R package for multivariate variable selection using genetic algorithms. Bioinformatics 22: 1154-1156.
21. Simon R, Lam A, Li MC, Ngan M, Menenzes S, et al. (2007) Analysis of Gene Expression Data Using BRB-Array Tools. Cancer Inform 3: 11-17.
22. Horng J, Wu L, Liu B, Kuo J, Kuo W, et al. (2009) An expert system to classify microarray gene expression data using gene selection by decision tree. Expert Systems with Applications 36: 9072-9081.
23. Tan PJ, Dowe DL, Dix TI (2007) Building classification models from microarray data with tree-based classification algorithms. In: Orgun MA, Thornton J, eds. Advances in Artificial Intelligence. Berlin, Heidelberg: Springer-Verlag. Pp 589-598.
24. Abeel T, Helleputte T, Van de PY, Dupont P, Saeys Y (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26: 392-398.
25. Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20: 2479-2481.
26. Platt JC (1998) Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf B, Burges CJC, Smola AJ, eds. Advances in Kernel Methods: Support Vector Learning. CambridgeMA: MIT Press. pp 185-208.
27. Zhang C, Li P, Rajendran A, Deng Y, Chen D (2006) Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data. BMC Bioinformatics 7 Suppl 4: S15.
28. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S (2005) A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21: 631-643.
29. McLachlan GJ, Chevelu J, Zhu J (2008) Correcting for selection bias via crossvalidation in the classification of microarray data. In: Balakrishnan N, Pena E, Silvapulle MJ, eds. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. Institute of Mathematical Statistics. Pp 364-376.
30. Yu C, Zavaljevski N, Desai V, Johnson S, Stevens FJ, et al. (2008) The development of PIPA: an integrated and automated pipeline for genome-wide protein function annotation. BMC Bioinformatics 9: 52.
31. Lu Y, Han J (2003) Cancer classification using expression data. Information Systems 28: 243-268.
32. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research 5: 1205-1224.
33. Boutros PC, Okey AB (2005) Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data. Brief Bioinform 6: 331-343.
34. Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23: 2507-2517.
35. Wang Y, Tetko IV, Hall MA, Frank E, Facius A, et al. (2005) Gene selection from microarray data for cancer classification-a machine learning approach. Comput Biol Chem 29: 37-46.
36. Rocke DM, Ideker T, Troyanskaya O, Quackenbush J, Dopazo J (2009) Papers on normalization, variable selection, classification or clustering of microarray data. Bioinformatics 25: 701-702.
37. Jeffery IB, Higgins DG, Culhane AC (2006) Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics 7: 359.
38. Han J, Kamber M (2006) Data Mining: Concepts and Technique. San FranciscoCA: Elsevier. pp 770.
39. Anand A, Suganthan PN (2009) Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. J Theor Biol 259: 533-540.
40. Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44: 1-12.
41. Suzuki T, Honda M, Matsumoto S, Sturzenbaum SR, Gamou S (2005) Valosine-containing proteins (VCP) in an annelid: identification of a novel spermatogenesis related factor. Gene 362: 11-18.
42. Mosser DD, Caron AW, Bourget L, Meriin AB, Sherman MY, et al. (2000) The chaperone function of hsp70 is required for protection against stress-induced apoptosis. Mol Cell Biol 20: 7146-7159.
43. Arcuri F, Papa S, Meini A, Carducci A, Romagnoli R, et al. (2005) Thetranslationally controlled tumor protein is a novel calcium binding protein of the human placenta and regulates calcium handling in trophoblast cells. Biol Reprod 73: 745-751.
44. Southby J, Gooding C, Smith CW (1999) Polypyrimidine tract binding proteinfunctions as a repressor to regulate alternative splicing of alpha-actinin mutally exclusive exons. Mol Cell Biol 19: 2699-2711.
45. Tonevitsky EA, Trushkin EV, Shkurnikov MU, Akimov EB, Sakharov DA (2009) Changed profile of splicing regulator genes expression in response to exercise. Bull Exp Biol Med 147: 733-736.
46. Jorgensen R, Merrill AR, Andersen GR (2006) The life and death of translation elongation factor 2. Biochem Soc Trans 34: 1-6.
47. Trabucchi M, Briata P, Garcia-Mayoral M, Haase AD, Filipowicz W, et al. (2009) The RNA-binding protein KSRP promotes the biogenesis of a subset of microRNAs. Nature 459: 1010-1014.
48. Song H, Mugnier P, Das AK, Webb HM, Evans DR, et al. (2000) The crystal structure of human eukaryotic release factor eRF1-mechanism of stop codon recognition and peptidyl-tRNA hydrolysis. Cell 100: 311-321.