ArticleBMC bioinformatics2008
A method for analyzing censored survival phenotype with gene expression data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Estimation of indirect effect when the mediator is a censored variable.Statistical methods in medical research · 2018Article
- Identification of human age-associated gene co-expressions in functional modules using liquid association.Oncotarget · 2018Article
- Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.BMC medical informatics and decision making · 2017Article
- Multiple functional linear model for association analysis of RNA-seq with imaging.Quantitative biology (Beijing, China) · 2015Article
- Article
- An efficient algorithm to explore liquid association on a genome-wide scale.BMC bioinformatics · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSurvival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. 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.
methodWe 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).
resultsThe 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.
conclusionBy an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.
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