Partially Supervised Learning Using an EM-Boosting Algorithm

Biometrics

Volumn 60, Issue 1, 2004, Pages 199-206

  Yasui, Yutaka ^a   Pepe, Margaret ^a,b   Hsu, Li ^a   Adam, Bao Ling ^c   Feng, Ziding ^a  

^a Fred Hutchinson Cancer Research Center   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c MEDICAL COLLEGE OF GEORGIA   (United States)

Author keywords

High dimensional data; Misclassification; Proteomics

Indexed keywords

BIOPSY; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; DISEASES; LEARNING ALGORITHMS; MASS SPECTROMETRY; MOLECULAR BIOLOGY; PROTEINS; TISSUE;

BIOMEDICAL SCIENCE; BOOSTING ALGORITHM; CLASS LABELS; HIGH DIMENSIONAL DATA; MISCLASSIFICATIONS; PARTIALLY-SUPERVISED LEARNING; PROTEOMICS; SCIENTIFIC APPLICATIONS; SUPERVISED LEARNING PROBLEMS; TRAINING DATA;

SUPERVISED LEARNING;

BIOLOGICAL MARKER;

MEDICINE; STATISTICAL ANALYSIS;

ALGORITHM; ARTICLE; BIOMEDICINE; BIOMETRY; BLOOD SAMPLING; CANCER CELL; CANCER DIAGNOSIS; CANCER PATIENT; CLINICAL PROTOCOL; CONTROLLED STUDY; DIAGNOSTIC ERROR; ERROR; HUMAN; LEARNING; MALE; MASS SPECTROMETRY; PREDICTION; PROSTATE CANCER; PROSTATE HYPERTROPHY; PROTEIN ANALYSIS; PROTEOMICS; SERUM; STANDARD; TRAINING; TUMOR BIOPSY;

EID: 1642306201     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2004.00156.x     Document Type: Article

References (26)

1. Adam, B. L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazaras, L. H., Semmes, O. J., Schellhammer, P. F., Yasui, Y., Feng, Z., and Wright, G. L., Jr. (2002). Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research 62, 3609-3614.
2. Brenner, H. and Gefeller, O. (1993). Use of the positive predictive value to correct for disease misclassification in epidemiologic studies. American Journal of Epidemiology 138, 1007-1015.
3. Chen, T. T. (1992). A review of methods for misclassified categorical data in epidemiology. Statistics in Medicine 8, 1095-1106.
4. Chu, C. K. and Cheng, K. F. (1995). Nonparametric regression estimates using misclassified binary responses. Biometrika 82, 315-325.
5. Copeland, K. T., Checkoway, H., McMichael, A. J., and Holbrook, R. H. (1977). Bias due to misclassification in the estimation of relative risk. American Journal of Epidemiology 105, 488-495.
6. DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44, 837-845.
7. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological), 39, 1-38.
8. Djavan, B., Mazal, P., Zlotta, A., Wammack, R., Ravery, V., Remzi, M., Susani, M., Borkowski, A., Hruby, S., Boccon-Gibod, L., Schulman, C. C., and Marberger, M. (2001). Pathological features of prostate cancer detected in initial and repeat prostate biopsy: Results of the prospective European Prostate Cancer Detection study. Prostate 47, 111-117.
9. Ekholm, A. (1991). Algorithms versus models for analyzing data that contain misclassification errors. Biometrics 47, 1171-1182.
10. Espeland, M. A. and Hui, S. L. (1987). A general approach to analyzing epidemiologic data that contain misclassification errors. Biometrics 43, 1001-1012.
11. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179-188.
12. Friedman, J. H., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics 28, 337-407.
13. Green, D. M. and Swets, J. A. (1966). Signal Detection Theory and Psychophysics. New York: John Wiley.
14. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
15. Magder, L. S. and Hughes, J. P. (1997). Logistic regression when the outcome is measured with uncertainty. American Journal of Epidemiology 146, 195-203.
16. McLachlin, J. J. (1992). Discriminant Analysis and Statistical Pattern Recognition. New York: John Wiley.
17. Neuhaus, J. M. (1999). Bias and efficiency loss due to misclassified responses in binary regression. Biometrika 86, 843-855.
18. Pepe, M. S., Etzioni, R., Feng, Z., Potter, J. D., Thompson, M. L., Thornquist, M., Winget, M.D., and Yasui, Y. (2001). Phases of biomarker development for early detection of cancer. Journal of the National Cancer Institute 93, 1054-1061.
19. Prescott, G. J. and Garthwaite, P. H. (2002). A simple Bayesian analysis of misclassified binary data with a validation substudy. Biometrics 58, 454-458.
20. Qu, Y., Adam, B. L., Yasui, Y., Ward, M. D., Cazares, L. H., Schellhammer, P. F., Feng, Z., Semmes, O. J., and Wright, G. L., Jr. (2002). Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clinical Chemistry 48, 1835-1843.
21. Quade, D., Lachenbruch, P. A., Whaley, F. S., McClish, D. K., and Haley, R. W. (1980). Effects of misclassifications on statistical inferences in epidemiology. American Journal of Epidemiology 111, 503-515.
22. Srivastava, S. and Kramer, B. S. (2000). Early detection cancer research network. Laboratory Investigation 80, 1147-1148.
23. Wang, C. Y. and Pepe, M. S. (2000). Expected estimating equations to accommodate covariate measurement error. Journal of the Royal Statistical Society, Series B 62, 509-524.
24. White, E. (1986). The effect of misclassification of disease status in follow-up studies: Implications for selecting disease classification criteria. American Journal of Epidemiology 124, 816-825.
25. Yasui, Y., McLerran, D., Adam, B. L., Winget, M., Thonquist, M., and Feng, Z. (2003a). An automated peak identification/calibration procedure for high-dimensional protein measures from mass spectrometers. Journal of Biomedicine and Biotechnology 2003, 242-248.
26. Yasui, Y., Pepe, M., Thompson, M. L., Adam, B. L., Wright, G. L., Jr., Qu, Y., Potter, J. D., Winget, M., Thornquist, M., and Feng, Z. (2003b). A data-analytic strategy for protein-biomarker discovery: Profiling of high-dimensional proteomic data for cancer detection. Biostatistics 4, 449-463.