Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

Theoretical Biology and Medical Modelling

Volumn 11, Issue SUPPL.1, 2014, Pages

  Luque Baena, Rafael Marcos ^a,b   Urda, Daniel ^a,b   Subirats, Jose Luis ^a,b   Franco, Leonardo ^a,b   Jerez, Jose M ^a,b  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b Biomedical Research Institute of Malaga IBIMA   (Spain)

Author keywords

Constructive neural networks; Feature Selection; Genetic algorithms; Microarray

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DNA MICROARRAY; FEMALE; GENETIC DATABASE; GENETICS; HUMAN; MALE; NEOPLASM; STATISTICS; TUMOR GENE;

ALGORITHMS; DATABASES, GENETIC; FEMALE; GENES, NEOPLASM; HUMANS; MALE; NEOPLASMS; NEURAL NETWORKS (COMPUTER); OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; STATISTICS AS TOPIC;

EID: 84900310308     PISSN: None     EISSN: 17424682     Source Type: Journal    
DOI: 10.1186/1742-4682-11-S1-S7     Document Type: Article

References (47)

1. Prediction of Clinical Outcome Using Gene Ex-pression Profiling and Artificial Neural Networks for Patients with Neuroblastom. Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Cancer Research 2004 64 6883 6891 10.1158/0008-5472.CAN-04-0695 15466177
2. Gene Expression Profiling in Polycythemia Vera Using cDNA Microarray Technology. Pellagatti A, Vetrie D, Langford CF, Gama S, Eagleton H, Wainscoat JS, Boultwood J, Cancer Research 2003 63 3940 3944 12873988
3. Bayesian factor regression models in the "large p, small n" paradigm. West M, Bayesian statistics 2003 7 2003 723 732
4. Rules of evidence for cancer molecular-marker discovery and validation. Ransohoff D, Nature Reviews Cancer 2004 4 4 309 314 10.1038/nrc1322 15057290
5. Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. Lancashire LJ, Rees RC, Ball GR, Artificial Intelligence In Medicine 2008 43 2 99 111 10.1016/j.artmed.2008.03.001 18420392
6. Optimal gene subset selection using the modified SFFS algorithm for tumor classification. Peng H, Fu Y, Liu J, Fang X, Jiang C, Neural Computing and Applications 2012 1 8
7. Dimensionality reduction using genetic algorithms. Raymer M, Punch W, Goodman E, Kuhn L, Jain A, IEEE Transactions on Evolutionary Computation 2000 4 2 164 171 10.1109/4235.850656
8. The application of ant colony optimization for gene selection in microarray-based cancer classification. Chiang Y, Chiang H, Lin S, Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC 2008 7 4001 4006
9. Object detection using feature subset selection. Sun Z, Bebis G, Miller R, Pattern Recognition 2004 37 11 2165 2176 10.1016/j.patcog.2004.03.013
10. A mixture model-based approach to the clustering of microarray expression data. McLachlan G, Bean R, Peel D, Bioinformatics 2002 18 3 413 422 10.1093/bioinformatics/18.3.413 11934740
11. Prediction error estimation: a comparison of resampling methods. Molinaro A, Simon R, Pfeiffer R, Bioinformatics 2005 21 15 3301 3307 10.1093/bioinformatics/bti499 15905277
12. Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood. Zhang F, Kaufman HL, Deng Y, Drabier R, BMC Medical Genomics 2013 6 1 23369435
13. Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data. Tong DL, Schierz AC, Artificial Intelligence in Medicine 2011 53 47 56 10.1016/j.artmed.2011.06.008 21775110
14. A dynamic subspace learning method for tumor classification using microarray gene expression data. Su Y, Wang R, Li C, Chen P, Proceedings-2011 7th International Conference on Natural Computation, ICNC 2011 2011 1 396 400
15. Stable feature selection and classification algorithms for multiclass microarray data. Student S, Fujarewicz K, Biology Direct 2012 7 23031190
16. Bioinformatics applications for pathway analysis of microarray data. Werner T, Current Opinion in Biotechnology 2008 19 50 54 10.1016/j.copbio.2007. 11.005 18207385
17. C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons. Subirats JL, Franco L, Jerez JM, Neural Networks 2012 26 130 140 22075034
18. A review of feature selection techniques in bioinformatics. Saeys Y, Inza I, Larranãga P, Bioinformatics 2007 23 19 2507 2517 10.1093/ bioinformatics/btm344 17720704
19. A hybrid LDA and genetic algorithm for gene selection and classification of microarray data. Huerta EB, Duval B, Hao JK, Neurocomputing 2010 73 2375 2383 10.1016/j.neucom.2010.03.024
20. The generalization of Student́s problem when several different population variances are involved. Welch BL, Biometrika 1947 34 1-2 28 35 10.1093/biomet/34.1-2.28 20287819
21. Webb AR, Statistical Pattern Recognition John Wiley & Sons 2 2011 third edition
22. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. Peng H, Long F, Ding C, IEEE Transactions on Pattern Analysis and Machine Intelligence 2005 27 8 1226 1238 16119262
23. Gait Feature Subset Selection by Mutual Information. Guo B, Nixon M, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 2009 39 36 46
24. On Estimation of Entropy and Mutual Information of Continuous Distributions. Moddemeijer R, Signal Processing 1989 16 3 233 246 10.1016/0165-1684(89)90132-1
25. A "thermal" perceptron learning rule. Frean M, Neural Comput. 1992 4 6 946 957 10.1162/neco.1992.4.6.946
26. A cooperative constructive method for neural networks for pattern recognition. García-Pedrajas N, Ortiz-Boyer D, Pattern Recogn. 2007 40 80 98 10.1016/j.patcog.2006.06.024
27. A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions. Subirats JL, Jerez JM, Franco L, IEEE Transactions on Circuits and Systems 2008 1 55 3188 3196
28. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. García S, Fernández A, Luengo J, Herrera F, Information Sciences 2010 180 10 2044 2064 10.1016/j.ins.2009.12.010
29. Comparative Effects of DHEA and DHT on Gene Expression in Human LNCaP Prostate Cancer Cells. Steele VE, Arnold JT, Le H, Izmirlian G, Blackman MR, Anticancer Research 2006 26 5A 3205 3215 17094431
30. Maspin acts at the cell membrane to inhibit invasion and motility of mammary and prostatic cancer cells. Sheng S, Carey J, Seftor EA, Dias L, Hendrix MJ, Sager R, Proc Natl Acad Sci USA 1996 93 21 11669 74 10.1073/pnas.93.21. 11669 8876194
31. HEPSIN Inhibits Cell Growth/Invasion in Prostate Cancer Cells. Srikantan V, Valladares M, Rhim JS, Moul JW, Srivastava S, Cancer Research 2002 62 23 6812 6816 12460890
32. {GSTP1} CpG Island Hypermethylation Is Responsible for the Absence of {GSTP1} Expression in Human Prostate Cancer Cells. Lin X, Tascilar M, Lee WH, Vles WJ, Lee BH, Veeraswamy R, Asgari K, Freije D, van Rees B, Gage WR, Bova GS, Isaacs WB, Brooks JD, DeWeese TL, Marzo AMD, Nelson WG, The American Journal of Pathology 2001 159 5 1815 1826 10.1016/S0002-9440(10)63028-3 11696442
33. Expression Profiles of Apoptotic Genes Induced by Curcumin in Human Breast Cancer and Mammary Epithelial Cell Lines. Ramachandran C, Rodriguez S, Ramachandran R, Nair PR, Fonseca H, Khatib Z, Escalon E, Melnick SJ, Anticancer Research 2005 25 5 3293 3302 16101141
34. Molecular characterization of breast cancer cell lines by expression profiling. Kroll T, Odyvanova L, Clement J, Platzer C, Naumann A, Marr N, Höffken K, Wölfl S, Journal of Cancer Research and Clinical Oncology 2002 128 3 125 134 10.1007/s00432-001-0308-z 11935298
35. Identification of brain-and bone-specific breast cancer metastasis genes. Klein A, Olendrowitz C, Schmutzler R, Hampl J, Schlag PM, Maass N, Arnold N, Wessel R, Ramser J, Meindl A, Scherneck S, Seitz S, Cancer Letters 2009 276 2 212 220 10.1016/j.canlet.2008.11.017 19114293
36. Proteomic Characterization of Novel Alternative Splice Variant Proteins in Human Epidermal Growth Factor Receptor 2 neu Induced Breast Cancers. Menon R, Omenn GS, Cancer Research 2010 70 9 3440 3449 10.1158/0008-5472.CAN-09-2631 20388783
37. Enforced CD19 Expression Leads to Growth Inhibition and Reduced Tumorigenicity. Mahmoud MS, Fujii R, Ishikawa H, Kawano MM, Blood 1999 94 10 3551 3558 10552966
38. HOXA9 Modulates Its Oncogenic Partner Meis1 To Influence Normal Hematopoiesis. Hu YL, Fong S, Ferrell C, Largman C, Shen WF, Molecular and Cellular Biology 2009 29 18 5181 5192 10.1128/MCB.00545-09 19620287
39. Surface expression and function of p75/AIRM-1 or CD33 in acute myeloid leukemias: Engagement of CD33 induces apoptosis of leukemic cells. Vitale C, Romagnani C, Puccetti A, Olive D, Costello R, Chiossone L, Pitto A, Bacigalupo A, Moretta L, Mingari MC, Proceedings of the National Academy of Sciences 2001 98 10 5764 5769 10.1073/pnas.091097198 11320212
40. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E, Science 1999 286 5439 531 537 10.1126/science.286.5439.531 10521349
41. A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data. Yang P, Zhou BB, Zhang Z, Zomaya AY, BMC Bioinformatics 2010 11 1 20122224
42. Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis. García-Nieto J, Alba E, Jourdan L, Talbi E, Information Processing Letters 2009 109 16 887 896 10.1016/j.ipl.2009.03.029
43. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. Krishnapuram B, Carin L, Hartemink A, Journal of Computational Biology 2004 11 2-3 227 242 10.1089/1066527041410463 15285890
44. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue. Chen Z, Li J, Wei L, Artificial Intelligence in Medicine 2007 41 2 161 175 10.1016/j.artmed.2007. 07.008 17851055
45. Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Shen Q, Shi WM, Kong W, Computational Biology and Chemistry 2008 32 53 60 10.1016/j.compbiolchem. 2007.10.001 18093877
46. Reduct Generation and Classification of Gene Expression Data. Momin B, Mitra S, Gupta R, Hybrid Information Technology, 2006 ICHIT '06 International Conference on 2006 1 699 708
47. Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms. Gwinn M, Keshava C, Olivero O, Humsi J, Poirier M, Weston A, OMICS 2005 9 4 334 50 10.1089/omi.2005.9.334 16402892