Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Sanguinetti, Guido
IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764
CiteScore 2018: 0.49
SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342
Mathematical Citation Quotient (MCQ) 2018: 0.02
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.
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