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

Autor:	Susmita Datta, Farida Mostajabi, Somnath Datta
Rok vydání:	2013
Předmět:	Statistics and Probability Elastic net regularization Proteomic Profile Lasso (statistics) Sample size determination Modeling and Simulation Statistics Partial least squares regression Linear regression Mass spectrometry Regression Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. 42:485-498
ISSN:	1532-4141 0361-0918
Popis:	Predicting survival times of patients with the proteomic profile of bodily fluids, such as plasma and serum, has been of interest in biomedical research. In this article, we consider the same with patient serum using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) data of non small cell lung cancer patients. Due to much larger dimension of features in a mass spectrum compared to the study sample size, traditional linear regression modeling of survival times with high number of proteomic features is not feasible. Hence, we consider latent factor and regularized/penalized methods for fitting such models in order to predict patient survival from the mass spectrometry features. Extensive numerical studies involving both simulated as well as real mass spectrometry data are used to compare four popular regression methods, namely, partial least squares (PLS), sparse partial least square (SPLS), least absolute shrinkage and selection operator (LASSO), and elastic net regularization, on proc...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2806aec1812ab8a53faaf8e4f5de075f https://doi.org/10.1080/03610918.2011.636165 Zobrazit plný text záznamu