Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses

Methods in Molecular Biology
Feng ChengJae K Lee

Abstract

Gene expression profiling technique now enables scientists to obtain a genome-wide picture of cellular functions on various human disease mechanisms which has also proven to be extremely valuable in forecasting patients' prognosis and therapeutic responses. A wide range of multivariate techniques have been employed in biomedical applications on such expression profiling data in order to identify expression biomarkers that are highly associated with patients' clinical outcome and to train multi-gene prediction models that can forecast various human disease outcome and drug toxicities. We provide here a brief overview on some of these approaches, succinctly summarizing relevant basic concepts, statistical algorithms, and several practical applications. We also introduce our recent in vitro molecular expression-based algorithm, the so-called COXEN technique, which uses specialized gene profile signatures as a Rosetta Stone for translating the information between two different biological systems or populations.

Citations

Oct 24, 2012·Journal of breast cancer·Sang-Hoon ChoSeung Il Kim
Feb 20, 2016·BMC Bioinformatics·Jared S FowlesDaniel L Gustafson
May 1, 2011·Personalized Medicine·Joanna Ashton-Chess, Alessandra Cl Cervino

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