Artificial Intelligence in Medicine
Volume 45, Issue 2 , Pages 163-171 , February 2009

Evaluating switching neural networks through artificial and real gene expression data

  • Marco Muselli

      Affiliations

    • Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, via De Marini 6, 16149 Genova, Italy
    • Corresponding Author InformationCorresponding author. Tel.: +39 010 6475213; fax: +39 010 6475200.
  • ,
  • Massimiliano Costacurta

      Affiliations

    • Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, via De Marini 6, 16149 Genova, Italy
  • ,
  • Francesca Ruffino

      Affiliations

    • Dipartimento di Scienze dell’Informazione, Università degli Studi di Milano, via Comelico 39 , 20135 Milano, Italy

Received 31 October 2007 ,Revised 1 August 2008 ,Accepted 4 August 2008.

References 

  1. Baldi P, Hatfield GW. DNA microarrays and gene expression. Cambridge, UK: Cambridge University Press; 2002;
  2. Chen JJ, Delongchamp RR, Tsai C-A, Hsueh H-M, Sistare F, Thompson KL. Analysis of variance components in gene expression data. Bioinformatics. 2004;20:1436–1446
  3. Ihmels J, Bergmann S, Barkai N. Defining transcription modules using large-scale gene expression data. Bioinformatics. 2004;20:1993–2003
  4. Lee MLT. Analysis of microarray gene expression data. Dordrecht, NL: Kluwer Academic Publishers; 2004;
  5. Quackenbush J. Computational analysis of microarray data. Nature Reviews Genetics. 2001;2:418–427
  6. Draghici S. Data analysis tools for DNA microarrays. Boca Raton: Chapman & Hall; 2003;
  7. Li L, Weinberg CR, Darden TA, Pedersen LG. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics. 2001;17:1131–1142
  8. Li T, Zhang C, Ogihara M. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics. 2004;20:2429–2437
  9. Xuan J, Wang Y, Dong Y, Feng Y, Wang B, Khan J, et al. Gene selection for multiclass prediction by weighted Fisher criterion. EURASIP Journal on Bioinformatics and Systems Biology 2007. doi:10.1155/2007/64628 [Article ID 64628].
  10. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
  11. Song L, Bedo J, Borgwardt KM, Gretton A, Smola A. Gene selection via the BAHSIC family of algorithms. Bioinformatics. 2007;23:i490–i498
  12. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences. 2002;99:6567–6572
  13. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536
  14. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences. 2001;98:5116–5121
  15. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology. 2004;3(1):Article 3
  16. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning. 2002;46:389–422
  17. Rakotomamonjy A. Variable selection using SVM-based criteria. Journal of Machine Learning Research. 2003;3:1357–1370
  18. Ding Y, Wilkins D. Improving the performance of SVM-RFE to select genes in microarray data. BMC Bioinformatics. 2006;7:12
  19. Zhang HH, Ahn J, Lin X, Park C. Gene selection using support vector machines with non-convex penalty. Bioinformatics. 2006;22:88–95
  20. Niijima S, Kuhara S. Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE. BMC Bioinformatics. 2006;7:543
  21. Muselli M. Switching neural networks: A new connectionist model for classification. In:  Apolloni B, et al. editor. WIRN/NAIS, vol. 3931 of lecture notes in computer science. Berlin: Springer-Verlag; 2006;p. 23–30
  22. Liu Q, Sung AH. Recursive feature addition for gene selection. In: Proceedings of the IJCNN06: international joint conference on neural networks; 2006. p. 1360–1367.
  23. Ruffino F, Muselli M, Valentini G. Gene expression modeling through positive Boolean functions. International Journal of Approximate Reasoning. 2007;47:97–108
  24. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D. Broad patterns of gene expressions revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Science USA. 1999;96:6745–6750
  25. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:503–511
  26. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nature Genetics. 2003;33:49–54
  27. Ye QH, Qin LX, Forgues M, He P, Kim JW, Peng AC. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nature Medicine. 2003;9(4):416–423
  28. Diggle P. Statistical analysis of spatial point patterns. London: Academic Press; 1983;

PII: S0933-3657(08)00104-8

doi: 10.1016/j.artmed.2008.08.002

Artificial Intelligence in Medicine
Volume 45, Issue 2 , Pages 163-171 , February 2009