Robust intervention in gene regulatory networks (TGen)
Sponsors: NSF, W. M. Keck Foundation
Description: This project involves the design of intervention strategies for altering the dynamics of gene regulatory networks that are robust with respect to modeling uncertainties.

Approximation of optimal control in gene regulatory network (TGen)
Sponsors: NSF, W. M. Keck Foundation
Description: To ease the curse of dimensionality, this project investigates approximation approaches to obtain sub-optimal control policies in the application of control to gene networks.

Application of optimal control to cancer therapy (TGen)
Sponsors: NSF
Description: The study applies control theory to find optimal treatment policies for cancer in the context of gene regulatory networks.

Inference of discrete gene regulatory networks (TGen, University of Texas at San Antonio, Strathclyde University)
Sponsors: NSF, NCI
Description: This effort consists of a number of approaches for designing gene regulatory networks, including using information theory, multiple data sources, and prior information in a Bayesian framework.

Inference of continuous-time gene regulatory networks (Texas A&M at Praireview)
Sponsors: NSF
Description: This project aims to construct continuous-time gene networks from time-course data.

Characterization of multivariate gene prediction (University of São Paulo, TGen)
Sponsors: University of São Paulo
Description: The study investigates the probabilistic characteristics of genes that lack predictive power for the system when observed alone but possess high predictive power when observed in combinations

Classifier error estimation (TGen)
Sponsors: NSF, NCI, University of Paris
Description: This effort involves several research thrusts, including studying the joint distribution between the true and estimated errors, and the design of new bolstering and convex estimators.

Feature selection with small samples (TGen)
Sponsors: TGen
Description: This study investigates the small-sample performance of feature-selection algorithms, such as their ability to find good feature sets and their susceptibility to peaking.

Context analysis for classification (TGen)
Sponsors: University of Paris, TGen
Description: This project analyzes the effects of mixed populations and proposes to find sub-samples which represent characterized populations, or contexts, within which classification is feasible.

Microarray compression (TGen)
Sponsors: NSF
Description: This study considers the effect of microarray image compression on phenotype classification and the detection of significant up- or down-regulated genes.