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A stochastic model for optimizing composite predictors based on gene expression profiles

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12880284&dopt=Abstract

Pharm Res. 2003 Jul;20(7):996-1000
Ramanathan M.
Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York 14260-1200, USA

PURPOSE:

This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics.

METHODS:

The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints.

The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a.

RESULTS:

The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite.

The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability.

The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level.

The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier."

When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method.

CONCLUSIONS:

The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.