Artificial Intelligence in Medicine
Volume 37, Issue 2 , Pages 85-109 , June 2006

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses

Received 19 October 2005 ,Revised 22 March 2006 ,Accepted 23 March 2006.

References 

  1. Ye Q, Qin L, Forgues M, He P, Kim J, Peng A, et al. Predicting hepatitis b virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.. Nat Med. 2003;9(4):416–423
  2. Ramaswamy S, Ross K, Lander E, Golub T. A molecular signature of metastasis in primary solid tumors. Nat Genet. 2003;33:49–54
  3. Yu Y, Khan J, Khanna C, Helman L, Meltzer P, Merlino G. Expression profiling identifies the cytoskeletal organizer ezrin and the developmental homoprotein Six-1 as key metastatic regulators. Nat Med. 2004;10(2):175–181
  4. Alizadeh A, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:503–511
  5. Rosenwald A, Wright R, Chan W, Connors JM, Campo RI, Fisher E, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma. New Engl J Med. 2002;346(25):1937–1947
  6. Dyrskjøt L, Thykjaer T, Kruhøffer M, Jensen J, Marcussen N, Hamilton-Dutoit S, et al. Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet. 2003;33:90–96
  7. Kotska D, Spang R. Finding disease specific alterations in the co-expression of genes. Bioinformatics. 2004;20(Suppl 1):i194–i199
  8. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA. 2004;101(3):811–816
  9. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998;95(25):14863–14868
  10. Shamir R, Sharan R. Algorithmic approaches to clustering gene expression data. In:  Jiang T,  Xu Y,  Zhang MQ editor. Current topics in computational biology. Boston, USA: MIT Press; 2001;p. 269–300
  11. Shipp M, Ross K, Tamayo P, Weng A, Kutok J, Aguiar R, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002;8(1):68–74
  12. Valentini G. Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles.. Artif Intell Med. 2002;26(3):283–306
  13. 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 USA. 2001;98(26):15149–15154
  14. Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, Ford JM, et al. Efficacy and safety of a specific inhibitor of the bcr-abl tyrosine kinase in chronic myeloid leukemia. New Engl J Med. 2001;344(14):1031–1037
  15. Furey TS, Cristianini N, Duffy N, Bednarski D, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16(10):906–914
  16. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7(6):673–679
  17. Yeang C, Ramaswamy S, Tamayo P, Mukherjee S, Rifkin RM, Angelo M, et al. Molecular classification of multiple tumor types. In: ISMB 2001, Proceedings of the 9th International Conference on Intelligent Systems for Molecular Biology, Special issue of Bioinformatics, vol 17, Suppl 1. Oxford University Press; 2001;p. 316–322
  18. Tung WL, Quek C. GenSo-FDSS: a neural-fuzzy decision support system for pediatric all cancer subtype identification using gene expression data. Artif Intell Med. 2005;33(1):61–88
  19. Alizadeh A, Ross DT, Perou CM, van de Rijn M. Towards a novel classification of human malignancies based on gene expression. J Pathol. 2001;195(1):41–52
  20. Jain AK, Murty MN, Flynn PJ. Data clustering: a review. Assoc Comput Mach Comput Surv. 1999;31(3):264–323
  21. Hastie T, Tibshirani R, Friedman R. The elements of statistical learning. New York: Springer; 2001;
  22. Kohonen T. The self-organizing map. Neurocomputing. 1998;21:1–6
  23. Chickering DM, Heckerman D. Efficient approximation for the marginal likelihood of Bayesian networks with hidden variables. Mach Learn. 1997;29:181–212
  24. Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL. Model-based clustering and data transformations for gene expression data. Bioinformatics. 2001;17(10):977–987
  25. Cheng Y, Church GM. Biclustering of expression data. In:  Altman R,  Bailey T,  Bourne P,  Gribskov M,  Lengauer T,  Shindyalov I, et al. editor. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology. 2000;p. 93–103
  26. Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: a survey. IEEE/Assoc Comput Mach Trans Comput Biol Bioinform. 2004;1(1):24–45
  27. Dudoit S, Fridlyand J. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 2002;3(7):1–21
  28. Dudoit S, Fridlyand J. Bagging to improve the accuracy of a clustering procedure. Bioinformatics. 2003;19(9):1090–1099
  29. Ben-Dor A, Shamir R, Yakhini Z. Clustering gene expression patterns. J Comput Biol. 1999;6(3):281–297
  30. Ben-Hur A, Ellisseeff A, Guyon I. A stability based method for discovering structure in clustered data. In:  Altman RB,  Dunker AK,  Hunter L,  Klein T,  Lauderdale K editor. Pacific symposium on biocomputing. vol 7:Lihue, Hawaii, USA: World Scientific; 2002;p. 6–17
  31. Yeung KY, Haynor DR, Ruzzo WL. Validating clustering for gene expression data. Bioinformatics. 2001;17(4):309–318
  32. Kerr MK, Curchill GA. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc Natl Acad Sci USA. 2001;98:8961–8965
  33. McShane LM, Radmacher D, Freidlin B, Yu R, Li MC, Simon R. Method for assessing reproducibility of clustering patterns observed in analyses of microarray data. Bioinformatics. 2002;18(11):1462–1469
  34. Smolkin M, Gosh. D. Cluster stability scores for microarray data in cancer studies. BMC Bioinformatics. 2003;36(4):
  35. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52:91–118
  36. Chen JJ, Delongchamp R, Tsai C, Hsueh H, Sisatare F, Thompson K, et al. Analysis of variance components in gene expression data. Bioinformatics. 2004;20(9):1436–1446
  37. Johnson WB, Lindenstrauss J. Extensions of Lipshitz mapping into Hilbert space. In: Conference in modern analysis and probability, vol 26 of Contemporary Mathematics. American Mathematical Society; 1984;p. 189–206
  38. Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20(8):832–844
  39. Indyk P. Algorithmic applications of low-distortion geometric embeddings. In: Proceedings of the 42nd IEEE symposium on Foundations of Computer Science. 2001;p. 10–33
  40. Achliopta D. Database-friendly random projections. In:  Buneman P editors. Proceedings of the Association for Computing Machinery Symposium on the Principles of Database Systems, Contemporary Mathematics. 2001;p. 274–281
  41. Bingham E, Mannila H. Random projection in dimensionality reduction: applications to image and text data. In:  Kohavi R,  Masand B editor. Proceedings of the Knowledge Discovery and Data Mining. 2001;
  42. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, et al. Classification of human lung carcinoma by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001;98(24):13790–13795
  43. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, et al. Molecular classification of malignant melanoma by gene expression profiling. Nature. 2000;406:536–540
  44. Martoglio A, Miskin J, Smith S, MacKay D. A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer. Bioinformatics. 2002;18(12):1617–1624
  45. Hadjitodorov S, Kuncheva L, Todorova L. Moderate diversity for better cluster ensembles. Inform. Fusion. 2005;Published on-line: http://www.sciencedirect.com (accessed: 23 March 2006)
  46. Windischberger C, Barth M, Lamm C, Schroeder L, Bauer H, Gur RC, et al. Fuzzy cluster analysis of high-field functional MRI data. Artif Intell Med. 2003;29(3):203–223
  47. Masulli F, Schenone A. A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif Intell Med. 1999;16:129–147
  48. Smith SP, Dubes R. Stability of a hierarchical clustering. Pattern Recogn. 1980;12:177–187
  49. Levine E, Domany E. Resampling method for unsupervised estimation of cluster validity. Neural Comput. 2001;13(11):2573–2593
  50. Milligan GW, Cooper MG. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 1985;50:159–179
  51. Chu S, DeRisi J, Eisen M, Mulholland J, Botstein J, Brown PO. Science. 1998;282:699–705
  52. McQueen J. Some methods for classification and analysis of multivariate observations. In:  LeCam LM,  Neyman J editor. Proceedings of the Fifth Berkeley Symposium of Mathematical Statistics and Probability. 1967;p. 281–297
  53. King B. Step-wise clustering procedures. J Am Stat Assoc. 1967;69:86–101
  54. Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58:236–244
  55. Valentini G. Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data. Bioinformatics. 2006;22(3):369–370
  56. Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S. Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer; 2005;
  57. Ghosh D, Chinnaiyan AM. Mixture modelling of gene expression data from microarray experiments. Bioinformatics. 2002;18(2):275–286
  58. Brafford P, Herlyn M. Gene expression profiling of melanoma cells: searching the haystack. J Transl Med. 2005;3;Published on-line: http://www.pubmedcentral.nih.gov/tocrender.fcgi?iid=18084 (accessed: 23 March 2006).

PII: S0933-3657(06)00036-4

doi: 10.1016/j.artmed.2006.03.005

Artificial Intelligence in Medicine
Volume 37, Issue 2 , Pages 85-109 , June 2006

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