Biodata Management and Analysis Study Section – BDMA

Topics

• Methods for data acquisition, storage, management, query and representation, data integrity and validation, and data integration.
• Methods for the analysis of large datasets from high throughput experiments including genomic, transcriptomic, nucleic acid:protein crosslinking-immunoprecipitation, proteomic and metabolomic studies.
• 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 Biomedical Computing and Health Informatics (BCHI) with regard to data mining of electronic medical records or integration of these records with other data sources. Applications that are developing new or improving the performance of existing computational strategies for mining and integrating electronic health records with ‘omics or imaging data can be assigned to BDMA. Applications that focus on new mining and integration methods and representing data for clinical decision support may be assigned to BCHI.

There are shared interests with Biomedical Imaging Technology A (BMIT-A) or Biomedical Imaging Technology B (BMIT-B) with regard to image analysis. 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 datasets, especially in a preclinical settings may be assigned to BMIT-A or BMIT-B.

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 improving -omic data analysis and integration may be assigned in BDMA. Applications developing computational methods to address specific biological questions in genetics, genomics, metabolomics, and proteomics 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 the improving or developing new algorithms or computational tools for general problems in network inference can be assigned in 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 computational methods and tools for specifically for improving brain imaging ‘-omics’ data analysis and integration with a focus on other biological processes are reviewed in BDMA. Applications that emphasize developing computational methods and tools for specifically for improving brain imaging ‘-omics’ data analysis and integration are reviewed in NICD.