Biodata Management and Analysis Study Section – BDMA
The Biodata Management and Analysis (BDMA) study section reviews grant applications focused on the development of novel computational methods for the acquisition, management, querying, sharing and analysis of biological data. Areas of interest overlap with basic research in computer science, statistics, computational biology and bioinformatics. Hypothesis-driven applications and applications incorporating limited experimentation are accepted when they are necessary to support or validate proposed analytical methods. Applications applying existing ML/AI tools to elucidate mechanistic questions are reviewed elsewhere.
Review Dates
Topics
- Methods for data acquisition, storage, management, query and representation, data integrity and validation, and data integration.
- Methods for analysis of large-scale, high throughput datasets, including genomic, epigenomic, transcriptomic, nucleic acid-protein interactions, proteomic and metabolomic data (HPC-based omics).
- Methods for pattern discovery, gene network inference, biomarker identification, protein and nucleic acid structure prediction, drug discovery and re-use.
- Scientific visualization systems for the summary, integration, and representation of data.
- Design and engineering of computing hardware and software systems for biological research involving but not limited to ‘omics, medical and cellular imaging, electronic medical records and biological simulations.
Shared Interests and Overlaps
There are shared interests with Clinical Data Management and Analysis (CDMA) in data integration, analysis and validation. Applications that focus on computational methods for analysis of biological data are reviewed in BDMA. Applications that emphasize the clinical translation or application of computational data mining methods/technology for clinical decision support are reviewed in CDMA.
There are shared interests with Emerging Imaging Technologies and Applications (EITA) in the analyses of image data. Applications that focus on the maintenance, refinement, or integration of existing software tools for imaging datasets can be assigned to BDMA. Applications that develop new data analysis methods and tools for imaging post-processing and display, feature detection, and classification, specifically in a clinical context, will be reviewed in EITA.
There are shared interests in statistical genetics and genomics with Analytics and Statistics for Population Research Panel A (ASPA). Applications that focus on computational methods for acquisition, management, querying, sharing and analysis of biological data, particularly software or computing hardware for the analysis of large genomic datasets, medical and cellular imaging are reviewed in BDMA. Applications that emphasize the development of statistical genetic and genomic methods and the development of statistical methods for scalable analysis of medical imaging and other diagnostic modality data for use in population-based research are reviewed in ASPA.
There are shared interests with Genomics, Computational Biology and Technology (GCAT) with regard to the computational analysis of –omics data. Applications that propose to develop computational methods and tools for omics- and multi-modal data integration may be assigned to BDMA. Applications developing computational methods focused predominantly on genomic data may be assigned to GCAT.
There are shared interests with Modeling and Analysis of Biological Systems (MABS), in particular with systems biology research. Applications that focus on improving or developing new algorithms or computational tools for general problems in network inference can be assigned to BDMA. Applications that have a specific biological focus can be assigned to MABS.
There are shared interests in the computational analysis of ‘-omics’ data with Neuro Infomatics, Computational and Data Analysis (NICD). Applications that emphasize developing general computational methods and tools for improving imaging ‘-omics’ data analysis and integration are reviewed in BDMA. Applications that emphasize developing computational methods and tools specifically for improving brain imaging ‘-omics’ data analysis and integration are reviewed in NICD.
