Digital Commons Network^™

Development Of Computations In Bioscience And Bioinformatics And Its Application: Review Of The Symposium Of Computations In Bioinformatics And Bioscience (Scbb06), Youping Deng, Jun Ni, Chaoyang Zhang

Faculty Publications

The first symposium of computations in bioinformatics and bioscience (SCBB06) was held in Hangzhou, China on June 21-22, 2006. Twenty-six peer-reviewed papers were selected for publication in this special issue of BMC Bioinformatics. These papers cover a broad range of topics including bioinformatics theories, algorithms, applications and tool development. The main technical topics contain gene expression analysis, sequence analysis, genome analysis, phylogenetic analysis, gene function prediction, molecular interaction and system biology, genetics and population study, immune strategy, protein structure prediction and proteomics.

Svm Classifier: A Comprehensive Java Interface For Support Vector Machine Classification Of Microarray Data, Mehdi Pirooznia, Youping Deng

Faculty Publications

Motivation

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction.

Results

The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries.

We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the …

Parallelization Of Multicategory Support Vector Machines (Pmc- Svm) For Classifying Microarray Data, Chaoyang Zhang, Peng Li, Arun Rajendran, Youping Deng, Dequan Chen

Faculty Publications

Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance computing techniques.

Results: In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM) have been developed based on the sequential minimum optimization-type decomposition method for support vector machines (SMO-SVM). It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster …