The following is the abstract to a poster that I am an author on being presented at National IDeA Symposium of Biomedical Research Excellence (NISBRE) August 6 - 9 in Washington D.C.
Neural Network Enhanced Visualization of High-Dimensional Data
Background and Objective
Large amounts of high-dimensional data not only create the need for the analysis of the data and interpretation of results, but also the need for the development of tools and methods that can handle such data. Many techniques are graphical in nature with ability to represent a small number of variables at a time. Application of information visualizations using neural network techniques enhance knowledge extraction and are targeted towards complex data and provide for a very small, if any, loss of information.
The algorithms are based on a self-organizing map (SOM) algorithm and are implemented in C/C++ programming language using OpenMP (shared memory) and MPI 2.0 (distributed memory) libraries for high-performance computing.