Combining clinical and genomic covariates via Cov-TGDR

Cancer Informatics

Volumn 3, Issue , 2007, Pages 381-388

  Ma, Shuangge ^a   Huang, Jian ^b  

^a YALE UNIVERSITY   (United States)

^b UNIVERSITY OF IOWA   (United States)

Author keywords

Classification; Microarray; Regularized estimation; Survival analysis

Indexed keywords

BIOLOGICAL MARKER;

ADULT; AGED; ARTICLE; BREAST CANCER; FEMALE; FOLLICULAR LYMPHOMA; GENOMICS; HUMAN; MAJOR CLINICAL STUDY; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; PROPORTIONAL HAZARDS MODEL; STATISTICAL MODEL; TUMOR GROWTH; ALGORITHM; CAUSE OF DEATH; COVARIATE ADJUSTED THRESHOLD GRADIENT DIRECTED REGULARIZATION; GENE EXPRESSION; GENETIC MARKER; GENETIC SELECTION; SURVIVAL PREDICTION; SURVIVAL RATE;

EID: 49649112616     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.1177/117693510700300015     Document Type: Article

References (18)

1. Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C. et al. 2000. Distinct types of diffuse large B-Cell lymphoma identified by gene expression profiling. Nature, 403:503-11.
2. Cox, D.R. 1972. Regression models and life-tables (with discussion). Journal of Royal Statistical Society, Series B., 34:187-220.
3. Dave, S.S., Wright, G., Tan, B. et al. 2004. Prediction of survival in follicular lym phoma based on molecular features of tumor-infiltrating immune cells. The New England Journal of Medicine, 351:2159-69.
4. Fernandez-Teijeiro, A., Betensky, R.A., Sturla, L.M., Kim, J., Tamayo, P. and Pomeroy, S.L. 2004. Combining gene expression profiles and clinical parameters for risk stratification in medulloblastomas. Journal of Clinical Ontology, 22:994-8.
5. Friedman, J. and Popescu, B.E. 2004. Gradient directed regularization. Technical Report, Stanford Department of Statistics.
6. Gajdos, C., Tartter, P. and Bleiweiss, I.J. 1999. Lymphatic invasion, tumor size, and age are independent predictors of axillary lymph node metastases in women with T1 breast cancers. Annals of Surgery, 230:692-6.
7. Ghosh, D. and Chinnaiyan, A.M. 2005. Covariate adjustment in the analysis of microarray data from clinical studies. Functional Integrative Genomics, 5:18-27.
8. Li, L. 2006. Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information. Bioinformatics, 22:466-71.
9. Ma, S. 2006. Empirical study of supervised gene screening. BMC Bioinformatics, 7:537.
10. Ma, S. and Huang, J. 2005. Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics, 21:4356-62.
11. Negri, E., Vecchia, C.L., Levi, F., Randriamiharisoa, A., Decarli, A. and Boyle, P. 2005. The application of age, period and cohort models to predict Swiss cancer mortality. Journal of Cancer Research and Clinical Ontology, 207-14.
12. Pittman, J., Huang, E., Dressman, H. et al. 2004. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. PNAS, 101:8431-6.
13. Rosenwald, A., Wright, G., Chan, W.C. et al. 2002. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England Journal of Medicine, 346: 1937-46.
14. Rosenwald, A., Wright, G., Wiestner, A., Chan, W.C. et al. 2003. The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell, 3:185-97.
15. Shipp, M.A., Ross, K.N., Tamayo, P. et al. 2002. Diffuse large-Bcell lymphoma outcome prediction by gene expression profiling and supervised machine learning. Nat Med., 8:68-74.
16. Sun, Y., Goodison, S., Li, J., Liu, L. and Farmerie, W. 2007. Improved breast cancer prognnosis through the combination of clinical and genetic markers. Bioinformatics, 23:30-7.
17. van't Veer, L.J., Dai, H., van de Vijver, M.J. et al. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415:530-6.
18. Wahba, G. 1990. Spline models for observational data. CBMS-NSF Regional Conference Series in Applied Mathematics.