Predicting Patient Survival from Proteomic Profile using Mass Spectrometry Data: An Empirical Study

DOI10.1080/03610918.2011.636165zbMath1433.62312OpenAlexW2056415526MaRDI QIDQ4921574

Susmita Datta, Somnath Datta, Farida Mostajabi

Publication date: 13 May 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.636165

zbMATH Keywords

dimension reductionregressionvariable selectionhigh dimensional dataproteomicnon small cell lung cancerregularized and penalized methods

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Testing in survival analysis and censored data (62N03)

Related Items (1)

A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics

Cites Work

• Unnamed Item
• Least angle regression. (With discussion)
• Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies
• Univariate Shrinkage in the Cox Model for High Dimensional Data
• Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates
• Regularization and Variable Selection Via the Elastic Net

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