An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++

PLoS ONE

Volumn 4, Issue 9, 2009, Pages

  Karpievitch, Yuliya V ^a   Hill, Elizabeth G ^b   Leclerc, Anthony P ^c   Dabney, Alan R ^a   Almeida, Jonas S ^d  

^a TEXAS A AND M UNIVERSITY   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^c School of Business   (United States)

^d UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BOOTSTRAPPING; CLASSIFIER; CLUSTER ANALYSIS; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER SIMULATION; CONTROLLED STUDY; ESOPHAGUS CANCER; INFORMATION PROCESSING; MATRIX ASSISTED LASER DESORPTION IONIZATION TIME OF FLIGHT MASS SPECTROMETRY; RANDOM FOREST; SAMPLE SIZE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER PROGRAM; DNA MICROARRAY; GENE EXPRESSION PROFILING; MASS SPECTROMETRY; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SOFTWARE; SPECTROMETRY, MASS, MATRIX-ASSISTED LASER DESORPTION-IONIZATION;

EID: 70349445083     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0007087     Document Type: Article

References (37)

1. Adam BL, Qu Y, Davis JW, Ward MD, Clements MA, et al. (2002) Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 62: 3609-3614.
2. Adam PJ, Boyd R, Tyson KL, Fletcher GC, Stamps A, et al. (2003) Comprehensive proteomic analysis of breast cancer cell membranes reveals unique proteins with potential roles in clinical cancer. J Biol Chem 278: 6482-6489.
3. Agranoff D, Fernandez-Reyes D, Papadopoulos MC, Rojas SA, Herbster M, et al. (2006) Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet 368: 1012-1021.
4. Chen YD, Zheng S, Yu JK, Hu X (2004) Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin Cancer Res 10: 8380-8385.
5. Conrads TP, Fusaro VA, Ross S, Johann D, Rajapakse V, et al. (2004) High-resolution serum proteomic features for ovarian cancer detection. Endocr Relat Cancer 11: 163-178.
6. Fung ET, Yip TT, Lomas L, Wang Z, Yip C, et al. (2005) Classification of cancer types by measuring variants of host response proteins using SELDI serum assays. Int J Cancer 115: 783-789.
7. Koomen JM, Shih LN, Coombes KR, Li D, Xiao LC, et al. (2005) Plasma protein profiling for diagnosis of pancreatic cancer reveals the presence of host response proteins. Clin Cancer Res 11: 1110-1118.
8. Li L, Tang H, Wu Z, Gong J, Gruidl M, et al. (2004) Data mining techniques for cancer detection using serum proteomic profiling. Artif Intell Med 32: 71-83.
9. Pawlik TM, Fritsche H, Coombes KR, Xiao L, Krishnamurthy S, et al. (2005) Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry. Breast Cancer Res Treat 89: 149-157.
10. Schaub S, Wilkins J, Weiler T, Sangster K, Rush D, et al. (2004) Urine protein profiling with surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry. Kidney Int 65: 323-332.
11. Vlahou A, Giannopoulos A, Gregory BW, Manousakas T, Kondylis FI, et al. (2004) Protein profiling in urine for the diagnosis of bladder cancer. Clin Chem 50: 1438-1441.
12. Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359: 572-577.
13. Garden RW, Sweedler JV (2000) Heterogeneity within MALDI samples as revealed by mass spectrometric imaging. Anal Chem 72: 30-36.
14. Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays. Nat Genet 32 Suppl: 490-495.
15. Dabney AR, Storey JD (2006) A reanalysis of a published Affymetrix GeneChip control dataset. Genome Biol 7: 401.
16. Breiman LE (2001) Random Forests. Machine Learning 45: 5-32.
17. Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and Regression Trees: New York: Chapman and Hall.
18. Efron B, Tibshirani RJ (1994) An Introduction to the Bootstrap: New York: Chapman and Hall.
19. Izmirlian G (2004) Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial. Ann N Y Acad Sci 1020: 154-174.
20. Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 8: 25.
21. Breiman L (1996) Bagging predictors. Machine Learning 24: 123-140.
22. Quinlan JR. Bagging, boosting, and C4.5; 1996. Meno Park: AAAI Press.
23. Palmer DS, O'Boyle NM, Glen RC, Mitchell JB (2007) Random forest models to predict aqueous solubility. J Chem Inf Model 47: 150-158.
24. Svetnik V, Liaw A, Tong C, Wang T. Application of Breiman's Random Forest to modeling structure-activity relationships of pharmaceutical molecules. In: Roli F, Kittler J, Windeatt T, eds. June 2004: proceedings; Cagliari, Italy. New York: Springer, pp 334-343.
25. Wu B, Abbott T, Fishman D, McMurray W, Mor G, et al. (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19: 1636-1643.
26. Finehout EJ, Franck Z, Choe LH, Relkin N, Lee KH (2007) Cerebrospinal fluid proteomic biomarkers for Alzheimer's disease. Ann Neurol 61: 120-129.
27. Hilario M, Kalousis A, Pellegrini C, Muller M (2006) Processing and classification of protein mass spectra. Mass Spectrom Rev 25: 409-449.
28. Rosner B (2000) Fundamentals of Biostatistics: Pacific Grove: Duxbury.
29. Karpievitch YV, Hill EG, Smolka AJ, Morris JS, Coombes KR, et al. (2007) PrepMS: TOF MS data graphical preprocessing tool. Bioinformatics 23: 264-265.
30. Karpievitch YV, Taverner T, Adkins JN, Callister SJ, Anderson GA, et al. (2009) Normalization of Peak Intensities in Bottom-Up MS-Based Proteomics Using Singular Value Decomposition. Bioinformatics.
31. Lee SK, Kang HK, Han ST, Kim KH (2005) Using Generalized Estimating Equation to Learn Decision Tree with Multivariate Responses. Data Mining and Knowledge Discovery 11: 273-293.
32. Segal MR (1992) Tree-Structured Methods for Longitudinal Data. Journal of the American Statistical Association 87: 407-418.
33. Zhang H (1998) Classification Trees for Multiple Binary Responses. Journal of the American Statistical Association 93: 180-193.
34. Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A (2008) Conditional variable importance for random forests. BMC Bioinformatics 9: 307.
35. Breiman L (1996) Technical Note: Some Properties of Splitting Criteria. Machine Learning 24: 41-47.
36. Morris JS, Coombes KR, Koomen J, Baggerly KA, Kobayashi R (2005) Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 21: 1764-1775.
37. Yasui Y, Pepe M, Thompson ML, Adam BL, Wright GL Jr, et al. (2003) A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics 4: 449-463.