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Heidi Spratt, PhD

Associate Professor, Department of Biochemistry & Molecular Biology,
and Department of Preventative Medicine & Community Health,
Bioinformatics Program,
Sealy Center for Molecular Medicine

Phone: (409) 747-6806

Heidi Spratt, PhD

Research Interests

My research area of interest in Bioinformatics which special considerations given to the mathematical discovery of biomarkers. Using the tools of mathematical modeling and machine learning, I am able to create classifiers to accurately predict disease outcome, discover the best treatment options for disease, or to examine disease prognosis. The methods of machine learning allow for multiple types of input, which make it extremely well suited for use in a clinical/translational setting. By utilizing both a training set of data and a testing set of data, one can check the accuracy of the classifier or model to build the best possible biomarker. Once the classifier has been created, researchers can use it to predict the outcome or best treatment options for future patients. I am also actively involved in developing methods to combine various types of data as well as optimizing the kernel functions which are at the heart of the machine learning algorithms.

Selected Publications

  1. Renwick, A., Davison, L., Spratt, H., King, P., and Kimmel, M. (2001) DNA Dinucleotide Evolution in Humans: Fitting Theory to Facts. Genetics, 159:737-747.

  2. Brasier, A., Spratt, H., Wu, Z., Boldogh, I., Zhang, Y., Garogalo, R., Casola, A., Pashmi, J., Haag, A., Luxon, B., and Kurosky, A. (2004) Nuclear Heat Shock Response and Novel Nuclear Domain 10 Reorganization in Respiratory Syncytial Virus-Infected A549 Cells Identified by High Resolution 2D Gel Electrophoresis, Journal of Virology, 78(21):11461-76.

  3. Forbus, J., Spratt, H., Wiktorowicz, J., Wu, Z., Boldogh, I., Denner, L., Kurosky, A., Brasier, R., Luxon, B., and Brasier, A. (2006) Functional Analysis of the Nuclear Proteome of Human A549 Alveolar Epithelial Cells by HPLC-High Resolution 2D Gel Electrophoresis, Proteomics, 6(9): 2656 – 2672.