Predicting patient survival from longitudinal gene expression.
Characterizing dynamic gene expression pattern and predicting patient outcome is now significant and will be of more interest in the future with large scale clinical investigation of microarrays. However, there is currently no method that has been developed for prediction of patient outcome using longitudinal gene expression, where gene expression of patients is being monitored across time. Here, we propose a novel prediction approach for patient survival time that makes use of time course structure of gene expression. This method is applied to a burn study. The genes involved in the final predictors are enriched in the inflammatory response and immune system related pathways. Moreover, our method is consistently better than prediction methods using individual time point gene expression or simply pooling gene expression from each time point.
Stanford University, USA. yupingz@stanford.edu
Yuping
Y
Zhang
Robert J
RJ
Tibshirani
Ronald W
RW
Davis
D000465:Algorithms; D015415:Biomarkers; D002056:Burns / Q000235:genetics* / Q000401:mortality*; D003627:Data Interpretation, Statistical; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D007249:Inflammation / Q000276:immunology; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011379:Prognosis; D016019:Survival Analysis; D013997:Time Factors
D015415:Biomarkers