Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
6 Issues per year
Increased IMPACT FACTOR 2012: 1.717
Rank 18 out of 117 in category Statistics & Probability in the 2012 Thomson Reuters Journal Citation Report/Science Edition
Mathematical Citation Quotient 2012: 0.07
Volume 12 (0)
Volume 11 (2012)
Volume 10 (2011)
Volume 9 (2010)
Volume 8 (2009)
Volume 6 (2007)
Volume 5 (2006)
Volume 4 (2005)
Volume 3 (2004)
Volume 2 (2003)
Volume 1 (2002)
Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- Normalization, bias correction, and peak calling for ChIP-seq by Diaz, Aaron/ Park, Kiyoub/ Lim, Daniel A. and Song, Jun S.
Predicting Patient Survival from Longitudinal Gene Expression
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 9, Issue 1, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1617, November 2010
- Published Online:
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.