A High performance cloud computing platform for mRNA analysis
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages
1510-1513
Date Issued
2013
Author(s)
Abstract
Multiclass classification is an important technique to many complex bioinformatics problems. However, their performance is limited by the computation power. Based on the Apache Hadoop design framework, this study proposes a two layer architecture that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have reduced 86.55% features and raised accuracy from 97.53% to 98.03%. With a user-friendly web interface, the system provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of bioinformatics data. ? 2013 IEEE.
Other Subjects
Bioinformatics data; Cloud computing platforms; Computation power; Design frameworks; Inherent parallelism; Layer architectures; Multi-class classification; Web interface; Bioinformatics; Computer software; Computer aided diagnosis; messenger RNA; algorithm; biology; genetics; human; procedures; theoretical model; time; Algorithms; Computational Biology; Humans; Models, Theoretical; RNA, Messenger; Time Factors
Type
conference paper