Advanced Biomedical Computational Science


The Advanced Biomedical Computational Science team focuses on applications of bioinformatics, computational and data science, and artificial intelligence to problems in cancer, infectious disease, immunology, rare disease, HIV and other specialized areas in biomedical research. 

Our scientists provide expertise, consultation, collaborative research, and project support in a broad range of computational and data science domains to Frederick National Laboratory, National Cancer Institute and National Institutes of Health and other federal researchers and staff. 

Our Advanced Biomedical Computational Science team encompasses specialized groups focusing on machine learning applied to the interpretation of 2D and 3D biomedical images, clinical and genomics integration with immunology, bioinformatic analysis of omics data, and other applications of computational and data science. 

In 2020, we won the medical imaging AI Grand Challenges on cancer tumor segmentation.  

Our groups regularly publish in top-tier journals on bioinformatics, immunology, imaging, computational chemistry, structural biology and knowledge integration. 


Data Solutions and Systems Biology Group 

  • Streamline and provide integrative and innovative solutions for the National Cancer Institute and National Institutes of Health community to access and use biological information collected across different sources and formats. 

  • Develop interactive solutions for disease-agnostic data sharing, analysis, variant impact annotations, identifier conversions across species, clinical-genomics integration and visualization of multi-modal biomedical data. 

  • Provide scientific infrastructure, scientific workflow management and innovative scientific web application and tool development support. 

  • Create applications and web interfaces for dynamic queries and interaction with biomedical data and scientific applications.

Imaging Visualization Group 

  • Support and accelerate basic research by developing and implementing technologies in image analysis and scientific visualization. 

  • Use machine learning and deep learning for digital pathology 3D electron microscopy, light microscopy, image volume registration, and web-based, real-time visualization. 

  • Facilitate data access, collaboration, and reuse to reduce duplicate efforts. 

Mathematical and Statistical Analysis Group 

  • Collaborate on projects requiring mathematical and statistical analysis, study design, visualization of study results, and modeling of cancer and HIV/AIDS. 

  • Create mathematical and statistical modeling for computational simulations, regression analysis, and survival analysis. 

  • Provide study design consultation. 

  • Provide training and outreach to increase awareness and understanding of statistics such as statistical reasoning seminars and tutorials, scientific programming seminars and tutorials, and software carpentry workshops. 

Simulation and Modeling Group 

  • Provide innovative solutions over a wide range of structure analysis and computational chemistry tools. 

  • Help accelerate the engineering and structural characterization of advanced materials and macromolecules. 

  • Develop tools and custom workflows for structural modeling including X-ray, electron microscopy, and protein structures and drug interactions. 

  • Provide quantum chemistry and drug design with small molecule properties obtained from high-level quantum chemical calculations. 

  • Develop algorithms and software for GAMESS. 

CCR and NIAID Collaborative Bioinformatics Resources and CCR Sequencing Facility Bioinformatics Groups 

  • Specialize in next-generation sequencing data analysis and quality control, sequencing technology consultation, exploration and assessment of new technologies, and data analysis and management for the National Cancer Institute’s Center for Cancer Research. 

  • Provide processing, analysis, and interpretation of high-dimensional data sets.

  • Benchmarked single-cell RNA sequencing technologies as part of a multicenter study.

  • Develop bioinformatics pipelines for next-generation sequencing quality assessment, and secondary and tertiary analysis.

  • Oversee the Bioinformatics Training and Education Program, providing classes and workshops featuring subject matter experts, and training offered for data visualization and both licensed and open-source applications. 


Machine learning and artificial intelligence 

  • Deep learning 

  • Digital Pathology 

  • NLP/Text mining 

Advanced structural studies 

  • Cryo-EM 

  • Electron diffraction 

 Advanced genomics technologies 

  • Single-cell sequencing  

  • Spatial transcriptomics  

  • Clinical translation 

Data integration technologies 

  • Graph databases