Bioinformatics and Computational Biosciences Branch’s (BCBB) Data Science and AI team focuses on maximizing the value and knowledge contained in your data to identify patterns, prioritize experimental variables, and perform predictive modeling.
Areas of Expertise
- Research methods and experimental design.
- Biostatistics and machine learning.
- Computational biology and artificial intelligence (AI).
Our data analysis methods include:
- Identifying patterns and outliers within numerical, text, or sequence-based data sets.
- Using 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.
Publications
Gabriel Rosenfeld (BCBB), et al. Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases. PLOS ONE. 2021. 16(3), e0247906.
Collaborator: Zhiyung Lu (NCBI). PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology. Bioinformatics. 2021. 37(13), 1884-1890.
Gabriel Rosenfeld (BCBB), et al. Current challenges in microbiome metadata collection. bioRxiv 2021.05.05.442781.
Collaborator: Patrick Duffy (LMIV). 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 (BCBB), et al. 2018. Deep Learning Improves Antimicrobial Peptide Recognition. Bioinformatics 34 (16), 2740–2747.
Research Team
Team Leads
Gabriel Rosenfeld, Ph.D.
Education:
Ph.D., 2013, Weill Cornell Graduate School of Medical Sciences, New York, NY
M.S., Developmental and Molecular Biology, Albert Einstein College of Medicine, Bronx, NY
B.S., Biology, 2005, SUNY Binghamton, Binghamton, NY
Gabriel Rosenfeld serves as Lead of Data Science in the Science Support Section in Bioinformatics and Computational Bioscience Branch (BCBB). He also contributes as subject matter expert to the TB Portals program, a trans-national partnership to use real-world data to study drug-resistant tuberculosis. He joined NIAID as a Presidential Management Fellow (PMF) in 2013, spent several years in...
Daniel Veltri, Ph.D.
Education:
Ph.D., 2015, George Mason University, Manassas, VA
M.S., 2013, George Mason University, Manassas, VA
B.A., 2006, University of Colorado at Boulder, Boulder, CO
Daniel Veltri is a bioinformatics data scientist and the federal lead for BCBB’s Clinical and Laboratory Informatics Systems group and co-lead of the Data Science, Biostatistics, and Informatics Support Team. He specializes in applying machine learning to clinical, genomic, and proteomic data and has the authored popular tools AMP Scanner for predicting antimicrobial peptides and SimpleSynteny for...
Team Members
Jingwen Gu, M.S.
(Contractor)
Education:
M.S., 2015, Georgetown University, Washington D.C.
Languages Spoken: Chinese
Jingwen primarily provides statistical support to NIAID researchers. She has experience in statistical genomics, machine learning, clinical trials, experimental design, causal inference, etc. While at BCBB, her work has focused on TB Portals and COVID-related research.
Mariam Namawejje, Ph.D.
(Contractor)
Education:
Ph.D., 2020, George Mason University, Manassas, VA
M.S., 2009, Makerere University, Kampala, UG
B.S., 2004, Makerere University, Kampala, UG
Mariam is a proficient data scientist specializing in bioinformatics and computational biology. With a strong background in statistics and information systems, she brings a comprehensive understanding of data analysis and programming to her work. Joining the Bioinformatics and Computational Biosciences Branch (BCBB) in 2020, she has a keen interest in leveraging this expertise to analyze complex...
Mina Peyton, Ph.D.
(Contractor)
Education:
Ph.D., 2022, University of Minnesota, Minneapolis, MN
M.S., 2022, University of Minnesota, Minneapolis, MN
Mina is a bioinformatics and computational biology specialist. She joined Bioinformatics and Computational Bioscience Branch (BCBB) in 2022. Her previous research experience was in proteomics, phosphoproteomics, aging, estrogen, and skeletal muscle. In her current role, she provides analytical support to NIAID researchers and ACE students on projects involving machine learning and biostatistical...
Yuyan Yi, Ph.D.
Education:
B.S., 2018, Jilin University, Changchun, Jilin, China
Ph.D., 2023, Auburn University, Auburn, AL
Languages Spoken: Chinese
With a background in statistics and data science, Yuyan applies machine learning and statistical methodologies across biomedical data. Ms. Yi provides analytical support to NIAID researchers, specializing in projects integrating machine learning and biostatistics. She also actively contributes to tuberculosis (TB) research, utilizing diverse real-world data for comprehensive biological and public...
