NEURAL NETWORK-ENHANCED STEREOSCOPIC VISUALIZATION

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Our goal is to develop algorithms and tools to advance large and multi-dimensional data processing methods geared towards biomedical data sets. They will be designed with the end user in mind, encompass powerful clustering techniques combined with visualizations, and will be easy to use and apply to any multivariate data set by an average user. Specific Aim 1 To fully implement the proposed algorithms for occlusion resolution (currently a working prototype). To promote portability, the algorithm will be implemented in Java programming language. Streamlining the development process, we will use either Borland JBuilder or Eclipse integrated development environment (IDE). Fully implementing the algorithm also means developing a complete design and documentation using Uniform Mark-up Language (UML). Specific Aim 2 Extend the model of neural-network enhanced visualizations from scatter plots to other visualizations and to provide support for distribution of data, providing data-driven mapping space for densely populated areas. Specific Aim 3 Extend the algorithms to three-dimensional space to allow for visual representation of high-dimensional data sets that are three dimensional in their nature (i.e., brain data sets, multiple replications of microarray experiments, etc.). Specific Aim 4 Introduce a model (based on Specific Aims 2 and 3) that supports the interpolation of data in a non-Euclidean representation (a SOM) and that same data in a Euclidean one (i.e., a scatter plot). Specific Aim 5 Develop a software package, coupled with visualization techniques and user interface tools that utilize proposed algorithms, tools, and techniques. The package will be based on robust statistical and computational techniques and governed by sound software engineering approaches. All software developed as part of this project will be made available to scientific community under GNU Public License.