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A survey of methods for classification of gene expression data using evolutionary algorithms

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Pages 101-110 | Published online: 09 Jan 2014

References

  • Quackenbush J. Computational analysis of microarray data. Nature Rev. Genet. 2(6), 418–427 (2001).
  • Speed TP. Statistical analysis of gene expression microarray data. Chapman & Hall/CRC, FL, USA (2003).
  • Stoughton RB. Applications of DNA microarrays in biology. Ann. Rev. Biochem. 74, 53–82 (2005).
  • Yang YH, Speed T. Design issues for cDNA microarray experiments. Nature Rev. Genet. 3(8), 579–588 (2002).
  • Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nature Genet. 32(Suppl.), 490–495 (2002).
  • Smyth GK, Yang YH, Speed T. Statistical issues in cDNA microarray data analysis. Methods Mol. Biol. 224, 111–136 (2003).
  • Draghici S, Khatri P, Eklund AC, Szallasi Z. Reliability and reproducibility issues in DNA microarray measurements. Trends Genet. (2006) (In Press).
  • Yang YH, Dudoit S, Luu P et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30(4), e15 (2002).
  • Hoffmann R, Seidl T, Dugas M. Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biol. 3(7), RESEARCH0033 (2002).
  • Holland MJ. Transcript abundance in yeast varies over six orders of magnitude. J. Biol. Chem. 277(17), 14363–14366 (2002).
  • Mecham BH, Klus GT, Strovel J et al. Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res. 32(9), e74 (2004).
  • Zhang J, Finney RP, Clifford RJ, Derr LK, Buetow KH. Detecting false expression signals in high-density oligonucleotide arrays by an in silico approach. Genomics 85(3), 297–308 (2005).
  • Ioannidis JP. Why most published research findings are false. PLoS Med. 2(8), e124 (2005).
  • van ‘t Veer LJ, Dai H, van de Vijver MJ et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002).
  • Ooi CH, Tan P. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19(1), 37–44 (2003).
  • Liu JJ, Cutler G, Li W et al. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 21(11), 2691–2697 (2005).
  • Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning : Data Mining, Inference, and Prediction. 14, Springer, NY, USA (2001).
  • Sorlie T, Perou CM, Tibshirani R et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98(19), 10869–10874 (2001).
  • Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365(9458), 488–492 (2005).
  • Wang Y, Klijn JG, Zhang T et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460), 671–679 (2005).
  • Haykin SS. Neural networks: a comprehensive foundation. Prentice Hall, NJ, USA (1999).
  • Ambroise C, McLachlan GJ. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl Acad. Sci. USA 99(10), 6562–6566 (2002).
  • Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5), 631–643 (2005).
  • Vapnik VN. Statistical Learning Theory. Wiley, NY, USA (1998).
  • Brown MP, Grundy WN, Lin D et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl Acad. Sci. USA 97(1), 262–267 (2000).
  • Rumelhart DE, Hinton GE, Williams RJ. Learning representations by backjpropagating errors. Nature 323, 533–536 (1986).
  • Khan J, Wei JS, Ringner M et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Med. 7(6), 673–679 (2001).
  • Li L, Pedersen LG, Darden TA, Weinberg C. Computational analysis of leukemia microarray expression data using the GA/KNN method. Proceedings of the First Conference on Critical Assessment of Microarray Data Analysis, CAMDA2000 (2000).
  • Li L, Pedersen LG, Darden TA, Weinberg C. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17(12), 1131–1142 (2001).
  • Li L, Darden TA, Weinberg CR, Levine AJ, Pedersen LG. Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Comb. Chem. High Throughput Screen. 4(8), 727–739 (2001).
  • Deutsch JM. Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 19(1), 45–52 (2003).
  • Eilers P, Boer J, Van Ommen GJ, Van Houwelingen H. Classification of microarray data with penalized logistic regression. In: Proceedings of SPIE, Progress in Biomedical Optics and Images, 4266, 187–198 (2001).
  • Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, CA, USA (1993).
  • Breiman L. Classification and Regression Trees. Wadsworth International Group, CA, USA (1984).
  • Livingston G, Li X, Li G, Hao L, Zhou J. Analyzing gene expression data using classification rules. In: The Twentieth International Conference on Machine LearningPavlovic V, Garg A, Kasif S (Eds). Washington, DC, USA (2003).
  • Breiman L. Random Forests. Machine Learning, 45, 5–32 (2001).
  • Ben-Dor A, Bruhn L, Friedman et al. Tissue classification with gene expression profiles. J. Comput. Biol. 7(3–4), 559–583 (2000).
  • Wahde M, Szallasi Z. The analysis of cancer-associated gene expression matrices. In: Foundations of Systems Biology. Kitano H (Ed.), MIT Press, MA, USA (2001).
  • Ramoni MF, Sebastiani P, Kohane IS. Cluster analysis of gene expression dynamics. Proc. Natl Acad. Sci. USA 99(14), 9121–9126 (2002).
  • Shipp, MA, Ross KN, Tamayo P et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Med. 8(1), 68–74 (2002).
  • Rosenwald A, Wright G, Chan WC et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346(25), 1937–1947 (2002).
  • Bäck T, Fogel DB, Michalewics Z. Handbook of Evolutionary Computation. Institute of Physics Publishing, Bristol, UK (1998).
  • Mitchell M. An Introduction to Genetic Algorithms. MIT Press, MA, USA (1996).
  • Fogel G, Corne D. Evolutionary Computation in Bioinformatics. Morgan Kaufmann, CA, USA (2003).
  • Holland JH. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, MI, USA (1975).
  • Liu J, Iba H, Ishizuka M. Selecting informative genes with parallel genetic algorithms in tissue classification. Genome Inform. Ser. Workshop Genome Inform. 12, 14–23 (2001).
  • Wahde M, Szallasi Z. Improving the prediction of the clinical outcome of breast cancer using evolutionary algorithms. Soft Computing (2005).
  • Golub TR, Slonim DK, Tamayo P et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (5439).
  • Alon U, Barkai N, Notterman DA et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA 96(12), 6745–6750 (1999).
  • Lin TC, Liu RC, Chen SY, Liu CC, Chen CY. Genetic algorithms and silhouette measures applied to microarray data classification. Proceedings of the 3rd Asia–Pacific Bioinformatics Conference. Imperial College Press, London, UK 229–238 (2005).
  • Ross DT, Scherf U, Eisen MB et al. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genet. 24(3), 227–235 (2000).
  • Peng S, Xu Q, Ling XB et al. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Lett.555(2), 358–362 (2003).
  • Lee JK, Bussey KJ, Gwadry FG et al. Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells. Genome Biol. 4(12), R82 (2003).
  • Kaufman L, Rousseeuw PJ. Finding Groups in Data : an Introduction to Cluster Analysis, Wiley, NY, USA (1990).
  • Deb K, Raji Reddy A. Reliable classification of two-class cancer data using evolutionary algorithms. Biosystems 72(1–2), 111–129 (2003).

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