Artificial Intelligence in Medicine
Volume 40, Issue 1 , Pages 29-44 , May 2007

Selection of relevant genes in cancer diagnosis based on their prediction accuracy

  • Rosalia Maglietta

      Affiliations

    • Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR Via Amendola 122/D-I, 70126 Bari, Italy
  • ,
  • Annarita D’Addabbo

      Affiliations

    • Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR Via Amendola 122/D-I, 70126 Bari, Italy
  • ,
  • Ada Piepoli

      Affiliations

    • Unità Operativa di Gastroenterologia, IRCCS, “Casa Sollievo della Sofferenza”-Ospedale, Viale Cappuccini, 71013 San Giovanni Rotondo (FG), Italy
  • ,
  • Francesco Perri

      Affiliations

    • Unità Operativa di Gastroenterologia, IRCCS, “Casa Sollievo della Sofferenza”-Ospedale, Viale Cappuccini, 71013 San Giovanni Rotondo (FG), Italy
  • ,
  • Sabino Liuni

      Affiliations

    • Istituto di Tecnologie Biomediche, Sede di Bari, CNR Via Amendola 122/D, 70126 Bari, Italy
  • ,
  • Graziano Pesole

      Affiliations

    • Istituto di Tecnologie Biomediche, Sede di Bari, CNR Via Amendola 122/D, 70126 Bari, Italy
    • Dipartimento di Biochimica e Biologia Molecolare, Universitá di Bari, Via E. Orabona 4, 70126 Bari, Italy
  • ,
  • Nicola Ancona

      Affiliations

    • Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR Via Amendola 122/D-I, 70126 Bari, Italy
    • Corresponding Author InformationCorresponding author. Tel.: +39 080 5929428; fax: +39 080 5929460.

Received 27 January 2006 ,Revised 1 June 2006 ,Accepted 6 June 2006.

References 

  1. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene-expression patterns with a complementary-DNA microarray. Science. 1995;270:467–470
  2. Yanai I, Benjamin H, Shmoish M, Chalifa-Caspi V, Shklar M, Ophir R, et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics. 2005;21(5):650–659
  3. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
  4. Ramaswamy S, Golub TR. DNA microarrays in clinical oncology. J Clin Oncol. 2001;20:1932–1941
  5. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, 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. 1999;96:6745–6750
  6. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci. 2001;98:15149–15154
  7. Birkenkamp-Demtroder K, Christensen LL, Olesen SH, Frederiksen CM, Laiho P, Aaltonen LA, et al. Gene expression in colorectal cancer. Cancer Res. 2002;62:4352–4363
  8. Wang Y, Jatkoe T, Zhang Y, Mutch MG, Talantov D, Jiang J, et al. Gene expression profiles and molecular markers to predict recurrence of Dukes’ B colon cancer. J Clin Oncol. 2004;22(9):1564–1571
  9. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422
  10. Slonim DK, Tamayo P, Mesirov JP, Golub TR, Lander ES. Class prediction and discovery using gene expression data. In:  Shamir R,  Miyano S,  Istrail S,  Pevzner P,  Waterman M editor. RECOMB ’00: Proceedings of the fourth annual international conference on computational molecular biology. New York, NY, USA: ACM Press; 2000;p. 263–272
  11. Furlanello C, Serafini M, Merler S, Jurman G. Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics. 2003;4(1):54
  12. Fu LM, Fu-Liu CS. Evaluation of gene importance in microarray data based upon probability of selection. BMC Bioinformatics. 2005;6:67
  13. Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V. Feature selection for svms. In:  Leen TK,  Dietterich TG,  Volker T editor. Advances in neural information processing systems. vol. 13:Cambridge, MA: MIT Press; 2001;p. 668–674
  14. Mukherjee S, Tamayo P, Rogers S, Rifkin R, Engle A, Campbell C, et al. Estimating dataset size requirements for classifying dna microarray data. J Comp Biol. 2003;10:119–142
  15. Fu WJ, Dougherty ER, Mallick B, Carroll RJ. How many samples are needed to build a classifier: a general sequential approach. Bioinformatics. 2005;21(1):63–70
  16. Li W, Sun F, Grosse I. Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression. J Comp Biol. 2004;11(2-3):215–226
  17. Vapnik V. The nature of statistical learning theory. New York: Springer Verlag Inc.; 1995;
  18. Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z. Tissue classification with gene expression profiles. J Comp Biol. 2000;7:559–583
  19. Evgeniou T, Pontil M, Poggio T. Regularization networks and support vector machines. Adv Comp Math. 2000;13(1):1–50
  20. Ambroise C, McLachlan GJ. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci. 2002;99:6562–6566
  21. Rifkin R, Yeo G, Poggio T. Regularized least squares classification. In: Suykens , Horvath , Basu , Micchelli , Vandewalle  editor. Advances in learning theory: methods, model and applications, NATO Science Series III: computer and systems sciences. vol. 90:Amsterdam: IOS Press; 2003;p. 131–153
  22. Zhang P, Peng J. Svm vs regularized least squares classification. In:  Kittler J,  Petrou M,  Nixon M editor. Proceedings of the 17th international conference on pattern recognition (ICPR ’04). Los Alamitos, CA, USA: IEEE Computer Society; 2004;p. 176–179
  23. Ancona N, Maglietta R, D’Addabbo A, Liuni S, Pesole G. Regularized least squares cancer classifiers from DNA microarray data. BMC Bioinformatics. 2005;6(Suppl 4):S2
  24. Golland P, Liang F, Mukherjee S, Panchenko D. Permutation tests for classification. In:  Auer P,  Meir P editor. Lecture notes in computer science. vol. 3559:Heidelberg: Springer Berlin; 2005;p. 501–515
  25. Good P. Permutation tests: a practical guide to resampling methods for testing hypotheses. New York: Springer Verlag Inc.; 1994;
  26. Nichols TE, Holmes AP. Non-parametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2001;15:1–25
  27. Tikhonov AN, Arsenin VY. Solutions of ill-posed problems. Washington, DC: W.H. Winston; 1977;
  28. Papoulis A. The Fourier integral and its applications. New York: McGraw-Hill Book Company; 1962;
  29. Luntz A, Brailovsky V. On estimation of characters obtained in statistical procedure of recognition. Technicheskaya Kibernetica. 1969;3
  30. Notterman DA, Alon U, Sierk AJ, Levine AJ. Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. Cancer Res. 2001;61:3124–3130
  31. Li Y, Campbell C, Tipping M. Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics. 2002;18:1332–1339
  32. Li JQ, Miki H, Ohmori M, Wu F, Funamoto Y. Expression of cyclin e and cyclin-dependent kinase 2 correlates with metastasis and prognosis in colorectal carcinoma. Hum Pathol. 2001;32:945–953
  33. Shiff SJ, Koutsos MI, Qiao L, Rigas B. Non-steroidal antiinflammatory drugs inhibit the proliferation of colon adenocarcinoma cells: effects on cell cycle and apoptosis. Exp Cell Res. 1996;222:179–188
  34. Lakshman M, Subramaniam V, Wong S, Jothy S. Cd44 promotes resistance to apoptosis in murine colonic epithelium. J Cell Physiol. 2005;203(3):583–588
  35. Antalis TM, Reeder JA, Gotley DC, Byeon MK, Walsh MD, Henderson KW, et al. Down-regulation of the down-regulated in adenoma (dra) gene correlates with colon tumor progression. Clin Cancer Res. 1998;4:1857–1863
  36. Bekku S, Mochizuki H, Yamamoto T, Ueno H, Takayama E, Tadakuma T. Expression of carbonic anhydrase i or ii and correlation to clinical aspects of colorectal cancer. Hepatogastroenterology. 2000;47:998–1001
  37. Nagel WW, Vallee BL. Cell cycle regulation of metallothionein in human colonic cancer cells. Proc Natl Acad Sci. 1995;17/92(2):579–583
  38. Strang G. Linear algebra and its applications. Wellesley, MA: Wellesley-Cambridge Press; 1988;

PII: S0933-3657(06)00097-2

doi: 10.1016/j.artmed.2006.06.002

Artificial Intelligence in Medicine
Volume 40, Issue 1 , Pages 29-44 , May 2007