NEURAL NETWORK-ENHANCED STEREOSCOPIC VISUALIZATION

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。我们的目标是开发算法和工具，以推进面向生物医学数据集的大型和多维数据处理方法。它们的设计将考虑到最终用户，包含强大的集群技术与可视化相结合，并且将易于使用和应用于普通用户的任何多变量数据集。具体目标1完全实现建议的遮挡解决算法(目前是工作原型)。为了提高可移植性，该算法将用Java编程语言实现。为了简化开发流程，我们将使用Borland JBuilder或Eclipse集成开发环境(IDE)。完全实现算法还意味着使用统一标记语言(Uniform Markup Language，UML)开发完整的设计和文档。具体目标2将神经网络增强可视化模型从散点图扩展到其他可视化，并为数据分布提供支持，为人口稠密地区提供数据驱动的测绘空间。具体目标3将算法扩展到三维空间，以允许视觉表示本质上是三维的高维数据集(即，脑数据集、微阵列实验的多次复制等)。具体目标4介绍了一个模型(基于具体目标2和3)，该模型支持非欧几里得表示(SOM)中的数据内插和欧几里得表示(即散点图)中的相同数据的内插。具体目标5开发一个软件包，结合使用所提出的算法、工具和技术的可视化技术和用户界面工具。该一揽子计划将以可靠的统计和计算技术为基础，并采用健全的软件工程方法进行管理。作为该项目的一部分开发的所有软件将根据GNU公共许可证向科学界提供。

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.