All entries by this author

Applications in Cancer Proteomic Biomarker Discovery and Validation

May 4th, 2014 | By

Researchers: Ulisses Braga-Neto This work, which is developed in collaboration with Drs. Edward Dougherty (Texas A&M), Michael Bittner (Translational Genomics), and Michelle Zhang (University of Texas at San Antonio), concerns the discovery and validation of proteomic biomarkers from protein-expression data, particularly data from high-throughput Liquid Chromatography-Mass Spectrometry (LC-MS) assays. This is part of the Partnesrhip for

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Small-Sample Classification and Error Estimation

May 4th, 2014 | By

Researchers: Ulisses Braga-Neto The research goals of this proposal concern the solution of significant computational and statistical problems in classification error estimation, with the purpose of improving the assessment of predictions made in classification and inference of genomic and proteomic signals based on small samples in high-dimensional spaces. This research critically impacts the discovery of reliable

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Discrete Prediction and Inference of Regulatory Networks

May 4th, 2014 | By

Researchers: Ulisses Braga-Neto This research concerns the modeling and inference of Boolean regulatory network models via the discrete Coefficient of Determination (CoD), with the goal of modeling complex dynamical processes occurring in living tissue, such as canalization of regulatory pathways and response to external stimuli.

Optimal State and Parameter Estimation for Boolean Dynamical Systems

Apr 29th, 2014 | By

Researchers: Ulisses Braga-Neto This research concerns a novel signal model for discrete-time Boolean dynamical systems under noisy observational conditions, which extends and unifies previously proposed models for biochemical regulatory networks, such as Boolean Network with perturbation (BNp) model and the Probabilistic Boolean Network (PBN) model. This novel signal model and its optimal state estimator, called

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Datta Short course on Engineering Applications in Genomics

Feb 3rd, 2014 | By

Aniruddha Datta conducted recently a 2-day course on “Engineering Applications in Genomics”. The videos of the course is available as seven lectures.

Interview to Aniruddha Datta in the IEEE Control Systems Magazine

Feb 3rd, 2014 | By

The Febuary issue of IEEE Control Systems Magazine presents a new interview to Aniruddha Datta, in its section “People In Control” “I really think that some of the most promising opportunities for control are in health-care applications. With an aging population and skyrocketing medical costs, I believe that these opportunities are here to stay for the foreseeable future. Although making useful contributions to such health-care

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Aniruddha Datta on Control Theory for Design of Cancer Therapy on

Dec 9th, 2013 | By

A new interview for, on Applying Control Theory to the Design of Cancer Therapy by Aniruddha Datta, is now available at the IEEE Life Sciences Portal.

New Positions Beginning in 2014

Nov 17th, 2013 | By

CBGSE expects to have approximately 8 new RA positions at the Ph.D. level beginning in Fall, 2014. We are looking for engineering graduates with strong mathematics backgrounds and interest in biological and medical applications.

Aniruddha Datta on An Engineering Approach to Cancer Therapy Design

Nov 8th, 2013 | By

A new article on An Engineering Approach to Cancer Therapy Design  by Aniruddha Datta is now available at the IEEE Life Sciences Portal. “Cancer encompasses various diseases associated with loss of control in the mechanisms that regulate the cell numbers in a multicellular organism. It is usually caused by malfunction(s) in the cellular signaling pathways. Malfunctions occur

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Texas A&M creates Texas-size genomic grant program

Oct 15th, 2013 | By
Texas A&M University Campus Photo Repository

Intending to empower the next generation of cutting-edge genomics research, members of The Texas A&M University System have contributed monies to create the largest internally funded genomics research grant program of its kind, with funds totaling $1.26 million, from Texas A&M University and its College of Agriculture and Life Sciences, Dwight Look College of Engineering, Division

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