Approaches to dimensionality reduction in proteomic biomarker studies

Briefings in Bioinformatics

Volumn 9, Issue 2, 2008, Pages 102-118

  Hilario, Melanie ^a   Kalousis, Alexandros ^b  

^a UNIVERSITY OF GENEVA   (Switzerland)

^b University's Artificial Intelligence Laboratory

Author keywords

Biomarkers; Dimensionality reduction; Feature selection; Feature transformation; Mass spectra; Proteomics

Indexed keywords

BIOLOGICAL MARKER;

BAYES THEOREM; CLASSIFICATION ALGORITHM; CONCEPTUAL FRAMEWORK; CORRELATION ANALYSIS; DATA ANALYSIS; DECISION TREE; DISCRIMINANT ANALYSIS; FOURIER TRANSFORMATION; GENETIC ALGORITHM; KNOWLEDGE DISCOVERY; LINEAR SYSTEM; MASS SPECTROMETRY; MATHEMATICAL COMPUTING; METHODOLOGY; MULTIVARIATE ANALYSIS; MULTIVARIATE LOGISTIC REGRESSION ANALYSIS; NORMAL DISTRIBUTION; PARTIAL LEAST SQUARES REGRESSION; PRINCIPAL COMPONENT ANALYSIS; PROTEIN DOMAIN; PROTEOMICS; REVIEW; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; AUTOMATIC DATA PROCESSING; BIOLOGICAL MARKERS; MASS SPECTROMETRY; PROTEOME; PROTEOMICS; RESEARCH;

EID: 42049102625     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbn005     Document Type: Review

References (98)

1. Somorjai RL, Dolenko B, Baumgartner R. Class prediction and discovery using gene microarray and proteomics mass spectrometry data: Curses, caveats, cautions. Bioinformatics 2003;19:1484-91.
2. Simon R. Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n). ACM SIGKDD Explor Newslett 2003;5:31-36.
3. Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell 1997;97:273-324.
4. Blum A, Langley P. Selection of relevant feaures and examples in machine learning. Artif Intell 1997;97: 245-71.
5. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157-82.
6. Papadopoulos M, Abel PM, Agranoff D, et al. A novel and accurate diagnostic test for human African trypanosomiasis. Lancet 2004; 363:1358-63.
7. Lilien RH, Farid H, Donald BR. Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum. J Comput Biol 2003;10: 925-946.
8. Qu Y, Adam BL, Thornquist M, et al. Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensional data. Biometrics 2003;59:143-51.
9. Duda R, Hart P, Stork D. Pattern Classification. Wiley, 2000; 117-24.
10. Wu B, Abbott T, Fishman D, et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 2003;19:1636-43.
11. Krzanowski W, Jonathan P, McCarthy W, et al. Discriminant analysis with singular covariance matrices: Methods and applications to spectroscopic data. Appl Stat 1995;44:101-15.
12. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001.
13. Boulesteix AL, Strimmer K. Partial least squares: A versatile tool for the analysis of high dimensional genomic data. Brief Bioinform 2006;8:32-44.
14. Dai JJ, Lieu L, Rocke D. Dimension reduction for classification with gene expression microarray data. Stat Appl Genet Mol Biol 2006; 5:6. http://www.bepress.com/sagmb/vol5/iss1/art6.
15. Lee KR, Lin X, Park DC, Eslava S. Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method. Proteomics 2003;3:1680-6.
16. Purohit PV, Rocke DM. Discriminant models for high-throughput proteomics mass spectrometer data. Proteomics 2003;3:1699-703.
17. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007;23:2507-17.
18. Wagner M, Naik D, Pothen A. Protocols for disease classification from mass spectrometry data. Proteomics 2003;3: 1692-8.
19. Rogers MA, Clarke P, Noble J, et al. Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: Identification of key issues affecting potential clinical utility. Cancer Res 2003;63 6971-83.
20. Kozak K, Amneus M, Pusey S, et al. Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: Potential use in diagnosis and prognosis. Proc Natl Acad Sci USA 2003;100 12343-8.
21. Sorace JM, Zhan M. A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinformatics 2003;4:24.
22. Yu J, Ongarello S, Fiedler R, et al. Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data. Bioinformatics 2005;21: 2200-9.
23. Levner I. Feature selection and nearest centroid classification for protein mass spectrometry. BMC Bioinformatics 2005;6:68.
24. Bhanot G, Alexe G, Venkataraghavan B, et al. A robust meta-classification strategy for cancer detection from mass spectra data. Proteomics 2006;6:592-604.
25. Cover T, Thomas J. Elements of Information Theory. Wiley, 1991.
26. Hilario M, Kalousis A, Muller M, et al. Machine learning approaches to lung cancer prediction from mass spectra. Proteomics 2003;3:1716-19.
27. Tukey JW. Tightening the clinical trial. Controll Clin Trials 1992;14:266-85.
28. Hedenfalk I, Duggan D, Chen Y, et al. Gene expression profiles in hereditary breast cancer. N Engl J Med 2001;344: 539-48.
29. Yanasigawa K, Shyr Y, Xu B, et al. Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 2003; 362 433-9.
30. Tibshirani K, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002;99: 6567-72.
31. Tibshirani R, Hastie T, Narasimhan B, et al. Sample classification from protein mass spectrometry by "peak probability contrasts". Bioinformatics 2004;20:3034-44.
32. Guyon I, Elisseeff A. Introduction. In: Guyon I, Gunn S, Nikravesh M, et al. (eds). Feature Extraction: Foundations, & Applications. Springer, 2006;1-25.
33. Reunanen J. Search Strategies. Chapter 4. In: Guyon I, Gunn S, Nikravesh M, et al. (eds). Feature Extraction: Foundations & Applications Springer, 2006.
34. Somol P, Pudil P, Novovicová J, Paclík P. Adaptive floating search methods in feature selection. Pattern Recogn Lett 1999; 20 1157-63.
35. Holland J. Adaptation in Natural, & Artificial Systems. MIT Press, 1992.
36. Dorigo M, Di Caro G, Gambardella L. Ant algorithms for discrete optimization. Artif Life 1999;5:137-72.
37. Kennedy J, Eberhart R. Particle swarm optimization. Proc. IEEE Int Conf Neural Netw. 1995;IV:1942-8.
38. Hall M, Holmes G. Benchmarking attribute selection techniques for discrete data class data mining. IEEE T Knowl Dat En 2003;15 1437-47.
39. Liu H, Li J, Wong L. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Inform 2002;13: 51-60.
40. Baggerly KA, Morris JS, Wang J, et al. A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization time of flight proteomics spectra from serum samples. Proteomics 2003;3 1667-72.
41. Kira K, Rendell L. The feature selection problem: Traditional methods and a new algorithm. Proc Natl Conf Artif Intell (AAAI-92) 1992;129-34.
42. Guyon I, Bitter HM, Ahmed Z, et al. Multivariate nonlinear feature selection with kernel multiplicative updates and Gram-Schmidt Relief. In: Proceedings of the BISCFLINT CIBI 2003 Workshop. CA: Berkeley, 2003.
43. Sun Y, Li J. Iterative Relief for feature weighting. In: Proceedings of the 23rd International Conference on Machine Learning. PA: Pittsburgh, 2006;913-20.
44. Breiman L, Friedman JH, Olshen RA, et al. Classification and Regression Trees. Belmont: Wadsworth, 1984.
45. Quinlan JR. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993,
46. Won Y, Song HJ, Kang TW, et al. Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons. Proteomics 2003;3: 2310-6.
47. Bañez LL, Prasanna P, Sun L, et al. Diagnostic potential of serum proteomic patterns in prostate cancer. J Urol 2003; 170 442-6.
48. Adam BL, Qu Y, Davis JW, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 2002;62:3609-14.
49. Freund Y, Schapire RE. Experiments with a new boosting algorithm. In: Proceedings of the 13th International Conference on Machine Learning. Bari, 1996; 148-56.
50. Qu Y, Adam BL, Yasui Y, et al. Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin Chem 2002;48:1835-43.
51. Yasui Y, Pepe M, Thompson M, et al. A data-analytic strategy for protein biomarker discovery: Profiling of high-dimensional proteomic data for cancer detection. Biostatistics 2003;4:449-63.
52. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum of identify ovarian cancer. Lancet 2002;359 572-7.
53. Kohonen T. Self-Organizing Maps. Springer-Verlag, 1995.
54. Li L, Tang H, Wu Z, et al. Data mining techniques for cancer detection using serum proteomic profiling. Artif Intell Med 2004;32:71-83.
55. Li L, Umbach DM, Terry P, et al. Application of the GA/KNN method to SELDI proteomics data. Bioinformatics 2004;20:1638-40.
56. Ressom H, Varghese R, Sahia D, et al. Particle swarm optimization for analysis of mass spectral serum profiles. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation(GECCO-05), Washington, DC, 2005;431-8.
57. Ressom H, Varghese R, Sahia D, et al. Analysis of mass spectral serum profiles for biomarker selection. Bioinformatics 2005;21 4039-45.
58. Ressom H, Varghese R, Drake S, et al. Peak selection from MALDI-TOF mass spectra using ant colony optimization. Bioinformatics 2007;23:619-26.
59. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;286:531-7.
60. Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines. Mach Learn 2002; 46:389-422.
61. Jong K, Marchiori E, Sebag M, et al. Feature selection in proteomic pattern data with support vector machines. In: Proceedings of the IEEE Symposium on Computational Intelligence in Bionformatics and Computational Biology. San Diego, CA, 2004;41-8.
62. Oh JH, Gao J, Nandi A, et al. Diagnosis of early relapse in ovariance cancer using serum proteomic profiling. Genome Inform 2005;16:195-204.
63. Zhang X, Lu X, Shi Q, et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 2006;7:197.
64. Tibshirani R. Regression shrinkage and selection via the Lasso. J. Roy Stat Soc B 1996;58:267-88.
65. Soltys S, Le Q, Shi G, et al. The use of plasma surface-enhanced laser desorption/ionization time-of-flight mass spectrometry proteomic patterns for detection of head and neck squamous cell cancers. Clin Cancer Res 2004;10: 4806-12.
66. Bhattacharya C, Grate L, Rizki A, et al. Simultaneous classication and relevant feature identication in high-dimensional spaces: application to molecular profiling data. Signal Process 2003;83 729-43.
67. Krishnapuram B, Carin L, Figuereido M, et al. Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005;27 957-968.
68. Pratapa PN, Patz E, Hartemink AJ. Finding diagnostic biomarkers in proteomic spectra. Pac Symp Biocomput 2006; 11:279-90.
69. Geurts P, Fillet M, de Sehy D, et al. Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics 2005;21:3138-45.
70. Ambroise C, McLachlan G. Selection bias in gene extraction on the basis of microarray gene-expression data. In: Proceedings of the Natl Acad Sci USA 2002;99:6562-6.
71. Kalousis A, Prados J, Hilario M. Stability of feature selection algorithms: A study on high-dimensional spaces. Knowl Inform Syst 2007;12:95-116.
72. Dietterich TG. Ensemble methods in machine learning. In: Kittler J. and Roli F (eds). Multiple Classifier Systems. Springer, 2000.
73. Tuv E. Ensemble Learning. In: Guyon I, Gunn S, Nikravesh M, et al. (eds). Feature Extraction: Foundations and Applications Springer, 2006;187-204.
74. Chan D, Bridges SM, Burgess S. An ensemble method for identifying robust features for biomarker identification. In: Liu H and Motoda H.(eds). Computational Methods of Feature Selection. Chapman & Hall/CRC, 2007;377-392.
75. Dutkowski J, Gambin A. On consensus biomarker selection. BMC Bioinformatics 2007;8(Suppl 5):5.
76. Achlioptas D. Database-friendly random projections. In: Proceedings of the ACM - Symposium on the Principles of Database Systems. Santa Barbara, CA, 2001;274-81.
77. Bingham E, Mannila H. Random projection in dimensionality reduction. Applications to image and text data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2001;245-50.
78. Johnscon WB, Lindenstrauss J. Extensions of Lipschitz mappings into a Hilbert space. In: Proceedings of the Conference in Modern Analysis and Probability. New Haven, CT, 1982;189-206.
79. Bertoni A, Valentini G. Model order selection for bio-molecular data clustering. BMC Bioinformatics 2007 8(Suppl 2):7.
80. Schölkopf B, Smola A, Müller KR. Kernel principal component analysis. In: Advances in Kernel Methods: Support Vector Learning. MIT Press, 1999;327-52.
81. Ng A. Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of the 21st International Conference on Machine Learning. Canada: Banff, 2004;78.
82. Zhu J, Rosset S, Hastie T, et al. 1-norm support vector machines. In: Advances in Neural Information Processing Systems 2004;16.
83. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B 2005;67:301-20.
84. Zou H, Hastie T. Model building and feature selection with genomic data. In: Liu and Motoda, (ed). Computational Methods of Feature Selection Chapman & Hall/CRC, 2007; 393-411.
85. p penalty for biomarker identification. Stat Appl Genet Mol Biol 2007;6:6.
86. Domeniconi C, Gunopoulos D. Adaptive nearest neighbor classification using support vector machines. In: Advances in Neural Information Processing (NIPS 14)MIT Press, 2002.
87. Goldberger A. Roweis S, Hinton G, et al. Neighbourhood component analysis. In: Advances in Neural Information Processing (NIPS 17). Cambridge, MA: MIT Press, 2005.
88. Weinberger K, Blitzer J, Saul L. Distance metric learning for large-margin nearest neighbor classification. In: Advances in Neural Information Processing (NIPS 18)MIT Press, 2006.
89. Xing E, Ng AY, Jordan MI, et al. Distance metric learning with application to clustering with side-information. In: Advances in Neural Information Processing (NIPS 15)MIT Press, 2003;505-13.
90. Bar Hillel A, Hertz T, Shental N, et al. Learning distance functions using equivalence relations. In: Proceedings of the International Conference on Machine Learning. Washington, DC, 2003;11-18.
91. Bishop C. Pattern Recognition and Machine Learning Springer, 2006.
92. Tipping ME. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 2001;1:211-44.
93. Sima C, Dougherty ER. What should be expected from feature selection in small-sample settings. Bioinformatics 2006;22:2430-6.
94. Guyon I, Aliferis C, Elisseeff A. Causal feature selection. In: Liu H and Motoda H (eds). Computational Methods of Feature Selection. Chapman & Hall/CRC, 2007;63-85.
95. Xing E, Jordan MI, Karp R. Feature selection for high-dimensional genomic microarray data. In: Proceedings of the 18th International Conference on Machine Learning. San Francisco: CA, 2001;601-8.
96. Friedman N, Linial M, Nachman I, et al. Using Bayesian networks to analyse expression data. In: Proceedings of the 4th Annual International Conference on Computational Molecular Biology (RECOMB). Tokyo, 2000; 127-35.
97. Tsamardinos I, Aliferis CF, Statnikov A. Algorithms for large-scale Markov blanket discovery. In: Proceedings-of the 16th International Florida Artificial Intelligence Research Society (FLAIRS) Conference. St Augustine: Florida, 2003; 376-80.
98. Aliferis CF, Tsamardinos I, Statnikov A. HITON, a novel Markov blanket algorithm for optimal variable selection. American Medical Informatics Association (AMIA) 2003;21-5.