Predicting patient survival from longitudinal gene expression

Statistical Applications in Genetics and Molecular Biology

Volumn 9, Issue 1, 2010, Pages

  Zhang, Yuping ^a   Tibshirani, Robert J ^a   Davis, Ronald W ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

gene expression; prediction; survival; time course

Indexed keywords

ARTICLE; BLOOD SAMPLING; BURN; CONTROLLED STUDY; CORRELATION ANALYSIS; GENE EXPRESSION; GENETIC ANALYSIS; GENETIC ASSOCIATION; HAZARD RATIO; HIGH RISK PATIENT; HUMAN; IMMUNE SYSTEM; INFLAMMATION; INTERMETHOD COMPARISON; LONGITUDINAL GENE EXPRESSION; LONGITUDINAL STUDY; MATHEMATICAL ANALYSIS; MOLECULAR DYNAMICS; MULTIPLE ORGAN FAILURE; OUTCOME ASSESSMENT; PATIENT MONITORING; PREDICTION; PROBABILITY; PROPORTIONAL HAZARDS MODEL; SIMULATION; SURVIVAL; SURVIVAL TIME; ALGORITHM; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETICS; IMMUNOLOGY; METHODOLOGY; MORTALITY; PROGNOSIS; STATISTICAL ANALYSIS; STATISTICS; TIME;

BIOLOGICAL MARKER;

ALGORITHMS; BIOLOGICAL MARKERS; BURNS; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMANS; INFLAMMATION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROGNOSIS; SURVIVAL ANALYSIS; TIME FACTORS;

EID: 78649920407     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.2202/1544-6115.1617     Document Type: Article

References (48)

1. Aastveit, A. H. and H. Martens (1986): "Anova interactions interpreted by partial least-squares regression", Biometrics, 829-844, 42 (4):.
2. Akdeniz, F. and G. Tabakan (2009): "Restricted ridge estimators of the parameters in semiparametric regression model", Communications in Statistics-Theory and Methods, 1852-1869, 38 (11):.
3. Bair, E., T. Hastie, and et al. (2006): "Prediction by supervised principal components", Journal of the American Statistical Association, 119-137, 101 (473):.
4. Bair, E. and R. Tibshirani (2004): "Semi-supervised methods to predict patient survival from gene expression data", PLoS Biol, 2(4):E108.
5. Chun, H. and S. Keles (2010): "Sparse partial least squares regression for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society Series B-Statistical Methodology, 3-25, 72:.
6. Cox, D. R. (1972): "Regression models and life tables", Journal of the Royal Statistical Society Series B, 187-220, 34 (2):.
7. Cramer, R. D., J. D. Bunce, and et al. (1988): "Cross-validation, bootstrapping, and partial least-squares compared with multiple-regression in conventional qsar studies", Quantitative Structure-Activity Relationships, 18-25, 7 (1):.
8. Desmedt, C., B. Haibe-Kains, and et al. (2009): "Gene expression signatures can predict the efficacy of anthracyclines in her2-negative and her2-positive breast cancer (bc) patients: The results of the top trial and their validation in the big1-00 trial, on behalf of the top trial investigators", Annals of Oncology, 45-45, 20:.
9. Faigle, J. F., R. J. Poppi, and et al. (1991): "Multicomponent principal component regression and partial least-squares analyses of overlapped chromatographic peaks", Journal of Chromatography, 123-132, 539 (1):.
10. Farkas, O. and K. R. Heberger (2005): "Comparison of ridge regression, partial least-squares, pairwise correlation, forward- and best subset selection methods for prediction of retention indices for aliphatic alcohols", Journal of Chemical Information and Modeling, 339-346, 45 (2):.
11. Forrester, J. B. and J. H. Kalivas (2004): "Ridge regression optimization using a harmonious approach", Journal of Chemometrics, 7-8, 18 (): 372-384.
12. Friedman, J., T. Hastie, and et al. (2010): "Regularization paths for generalized linear models via coordinate descent, journal of statistical software 33(1)",.
13. Gana, R. (1995): "Ridge-regression estimation of the linear probability model journal of applied statistics 22(4):537-539",.
14. Helland, I. S. (1990): "Partial least-squares regression and statistical-models", Scandinavian Journal of Statistics, 97-114, 17 (2):.
15. Hertz, D. (1991): "Sequential ridge-regression", Ieee Transactions on Aerospace and Electronic Systems, 571-574, 27 (3):.
16. Hoerl, A. E. and R. W. Kennard (1990): "Ridge-regression - degrees of freedom in the analysis of variance", Communications in Statistics-Simulation and Computation, 1485-1495, 19 (4):.
17. Holcomb, T. R., H. Hjalmarsson, and et al. (1997): "Significance regression: A statistical approach to partial least squares", Journal of Chemometrics, 283-309, 11 (4):.
18. Hu, H. C. (2005): "Ridge estimation of a semiparametric regression model", Journal of Computational and Applied Mathematics, 215-222, 176 (1):.
19. Jacobsen, T., K. Kolset, and et al. (1986): "Partial least-squares regression and fuzzy clustering - a joint approach", Mikrochimica Acta, 1-6, 2:125-138.
20. Jain, R. K. (1985): "Ridge regression and its application to medical data", Comput Biomed Res, 363-368, 18 (4):.
21. Karlsson, E., U. Delle, and et al. (2008): "Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer", Bmc Cancer, 8.
22. Korde, L. A., L. Lusa, and et al. (2010): "Gene expression pathway analysis to predict response to neoadjuvant docetaxel and capecitabine for breast cancer", Breast Cancer Research and Treatment, 685-699, 119 (3):.
23. Lecessie, S. and J. C. Vanhouwelingen (1992): "Ridge estimators in logisticregression", Applied Statistics-Journal of the Royal Statistical Society Series C, 191-201, 41 (1):.
24. Li, C. and W. H. Wong (2001): "Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection, proc natl acad sci u s a 98(1):31-36",.
25. Lin, Y. H., J. Friederichs, and et al. (2007): "Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer", Clinical Cancer Research, 498-507, 13 (2):.
26. Ma, P., W. Zhong, and J. S. Liu (2009): "Identifying differentially expressed genes in time course microarray data", Statistics in Biosciences, 144-159, 1 (2):.
27. Martens, I. L., H. and et al. (1986): "Partial least-squares regression on design variables as an alternative to analysis of variance", Analytica Chimica Acta, 133-148, 191:.
28. Maruyama, Y. and W. E. Strawderman (2005): "A new class of generalized bayes minimax ridge regression estimators", Annals of Statistics, 1753-1770, 33 (4):.
29. Naderi, A., A. E. Teschendorff, and et al. (2007): "A gene-expression signature to predict survival in breast cancer across independent data sets", Oncogene, 1507-1516, 26 10):.
30. Nguyen, D. V. and D. M. Rocke (2002): "Partial least squares proportional hazard regression for application to dna microarray survival data", Bioinformatics, 1625-1632, 18 (12):.
31. Nimeus-Malmstrom, E., C. Ritz, and et al. (2006): "Gene expression profilers and conventional clinical markers to predict distant recurrences for premenopausal breast cancer patients after adjuvant chemotherapy", European Journal of Cancer, 2729-2737, 42 (16):.
32. Ozkale, M. R. (2008): "A jackknifed ridge estimator in the linear regression model with heteroscedastic or correlated errors", Statistics and Probability Letters, 3159-3169, 78 (18):.
33. Pandit, T. S., W. Kennette, and et al. (2009): "Lymphatic metastasis of breast cancer cells is associated with differential gene expression profiles that predict cancer stem cell-like properties and the ability to survive, establish and grow in a foreign environment", International Journal of Oncology, 297-308, 35 (2):.
34. Park, M. and T. Hastie (2007): "L1-regularization path algorithm for generalized linear models", Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 659-677.
35. Pliskin, J. L. (1990): "The mean squared error of the generalized ridge-regression estimator and the orientation of beta", Communications in Statistics-Simulation and Computation, 1477-1484, 19 (4):.
36. Ryan, C. M., D. A. Schoenfeld, and et al. (1998): "Objective estimates of the probability of death from burn injuries, n engl j med 338(6):362-366",.
37. Schneeweiss, A., P. Lichter, and et al. (2006): "Gene expression profiling to predict chemotherapy response in primary breast cancer", Breast Care, 362-367, 1 (6):.
38. Schorge, J. O., D. R. D., and et al. (2009): "Gene expression profiling to predict early relapse in ovarian cancer", Gynecologic Oncology, 112(2):S105-S106.
39. Storey, J. D., W. Xiao, and et al. (2005): "Significance analysis of time course microarray experiments", Proc Natl Acad Sci U S A, 2837-1284, 102(36):12.
40. Tai, S. T., YC (2006): "A multivariate empirical bayes statistic for replicated microarray time course data", Annals of Statistics, 2387-2412, 34:.
41. Tibshirani, R. (1996): "Regression shrinkage and selection via the lasso", Journal of the Royal Statistical Society Series B-Methodological, 267-288, 58 (1):.
42. Vangaans, P. F. M. and S. P. Vriend (1990): "Multiple linear-regression with correlations among the predictor variables - theory and computer algorithm ridge (fortran-77)", Computers and Geosciences, 933-952, 16 (7):.
43. Wakeling, I. N. and J. J. Morris (1993): "A test of significance for partial leastsquares regression journal of chemometrics 7(4):291-304",.
44. Yeh, C. H. and C. H. Spiegelman (1994): "Partial least-squares and classification and regression trees chemometrics and intelligent laboratory systems 22(1):17-23",.
45. Young, P. J. (1994): "A reformulation of the partial least-squares regression algorithm", Siam Journal on Scientific Computing, 225-230, 15 (1):.
46. Yuan, K. C., M. (2006): "Hidden markov models for microarray time course data under multiple biological conditions (with discussion)", Journal of the American Statistical Association, 1323-1340, 101:.
47. Yuan, Y. Y., C. T. Li, and et al. (2008): "Partial mixture model for tight clustering of gene expression time-course", Bmc Bioinformatics, 9.
48. Zhou, B., W. Xu, D. Herndon, R. Tompkins, R. Davis, W. Xiao, and W. Wong (2010): "Analysis of factorial time-course microarrays with application to a clinical study of burn injury", Proceedings of the National Academy of Sciences, 107, 9923.