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Biomedical Computing

We are developing methods for interacting with data and algorithms. These will include the development of interactive visual environments for data processing, where methods can be programmed. We have already developed several software components for viewing various imaging modalities and are working on extending these viewers to provide greater interaction among our tools and better simultaneous presentation of multiple modalities of data.

Pipeline
We are continuing to develop the Pipeline Processing Environment and are extending it to incorporate the SCIRun Environment.

Computational Tools
We are developing new computational tools for integrating, managing, modeling, and visualizing neuroimaging and neurogenetic data.

Pipeline
Visual programming environments allow users to view the stages of algorithms. We have previously developed the LONI Pipeline Processing Environment and the SCIRun Problem Solving Environment (PSE). By extending the Pipeline to incorporate SCIRun, we increase the level of granularity at which the user can control a data process. This also increases the availability of visualization methods within the CCB Pipeline Environment. We are developing intelligent algorithms to automatically launch viewers that are appropriate for a given data type. Similarly, the Pipeline Environment is available within SCIRun, allowing users access to a broad collection of algorithms. CCB pipeline processing protocols have been introduced that wrap our current perl and script language commands for gene splicing, cross-species homology computation and sequence alignment into graphical Pipeline and SCIRun modules for automated and robust genetic modeling.

Computational Tools
We have developed a score of novel computational tools employed routinely in the areas of brain mapping and neurogenetics. Although other groups have designed similar tools, there remains a great need to introduce new methods that bridge the gap between these two disciplines and provide the means of correlating and merging neuroimaging and genetics data.

Multidimensional visualization, image processing, biosequence analysis, and database management are symbiotic components. Linking imaging and genetics data is being established by providing look up-tables between stereotactic atlas coordinates and known lists of genes expressed in the corresponding anatomical location (e.g., in-situ hybridization). We are creating and managing these tables. Gene expression patterns, intensities, interactions, homologies, and other gene specific information will not be managed by our resource. We are utilizing these meta-data directly from their remote home-database sites, which store and manage each particular gene description. Based on the mapping between voxel location and the corresponding genotypic signature, neuroinformatics and data-mining techniques can correlate regional, temporal, and disease-specific alterations in brain anatomy with genetic levels of expression.