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However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>We propose a method for an integrated study of survival time and gene expression. This method can be summarized as a two-step procedure: in the first step, a moderate number of genes are pre-selected using correlation or liquid association (LA). Imputation and transformation methods are employed for the correlation\/LA calculation. In the second step, the dimension of the predictors is further reduced using the modified sliced inverse regression for censored data (censorSIR).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The new method is tested via both simulated and real data. For the real data application, we employed a set of 295 breast cancer patients and found a linear combination of 22 gene expression profiles that are significantly correlated with patients' survival rate.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>By an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-9-417","type":"journal-article","created":{"date-parts":[[2008,10,7]],"date-time":"2008-10-07T06:14:09Z","timestamp":1223360049000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A method for analyzing censored survival phenotype with gene expression data"],"prefix":"10.1186","volume":"9","author":[{"given":"Tongtong","family":"Wu","sequence":"first","affiliation":[]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Shinsheng","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Chun-Houh","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Ker-Chau","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,10,6]]},"reference":[{"issue":"5439","key":"2402_CR1","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. 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