Data Science and Biostatistics

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Data Science is used to maximize the value and knowledge contained in your data to identify patterns, prioritize experimental variables, and perform predictive modeling.

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Our Data Analysis Methods include:

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• Identify patterns and outliers within numerical, text, or sequence-based data sets.
• Use AI/ML algorithms and experimental design.
• Data processing (e.g. feature engineering, transformation, normalization and imputation)
• Developing and evaluating supervised and unsupervised machine learning models.
• Variable ranking and prioritization.
• Deep Learning and AI approaches.
• Reproducible data science workflows.
• Statistical Testing and Power Analysis.

Data Science and Biostatistics Team

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• Gabe Rosenfeld, Ph.D. (Group coordinator)
• Dan Veltri, Ph.D. (Group coordinator)
• Jingwen Gu, M.S.
• Mariam Namawejje, Ph.D.
• Mina Peyton, Ph.D.

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Selected Publications

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• Rosenfeld, G., Gabrielian, A., Wang, Q., Gu, J., Hurt, D., Long, A., & Rosenthal, A. (2021). Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases. PLOS ONE, 16(3), e0247906.
• Luo, L., Yan, S., Lai, P., Veltri, D., Oler, A., & Xirasagar, S. et al. (2021). PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.  Bioinformatics, 37(13), 1884-1890.
• Gabriel Rosenfeld, Angelina Angelova, Chris Shin, Mariam Quinones, Darrell Hurt. (2021). Current challenges in microbiome metadata collection. bioRxiv 2021.05.05.442781
• K Singh, M Burkhardt, S Nakuchima, R Herrera, et al. 2020. "Structure and function of a malaria transmission blocking vaccine targeting Pfs230 and Pfs230-Pfs48/45 proteins" Communications Biology 3 (395), 1-12.
• D. Veltri, U. Kamath, A. Shehu. 2018. "Deep Learning Improves Antimicrobial Peptide Recognition" Bioinformatics 34 (16), 2740–2747.