A High performance cloud computing platform for mRNA analysis

Author

Feng-Seng Lin ; Chia-Ping Shen ; Hsiao-Ya Sung ; Yan-Yu Lam ; Jeng-Wei Lin ; Feipei Lai

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan

fYear

2013

fDate

3-7 July 2013

Firstpage

1510

Lastpage

1513

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.

Keywords

Internet; RNA; bioinformatics; cancer; cloud computing; genetic algorithms; human computer interaction; molecular biophysics; pattern classification; public domain software; support vector machines; Apache Hadoop design framework; GA-SVM classification; bioinformatics data; computation power; genetic algorithm; high performance cloud computing platform; mRNA benchmark cancer dataset; multiclass classification; support vector machines; two layer architecture; user-friendly Web interface; Accuracy; Bioinformatics; Biological cells; Computer architecture; Genetic algorithms; Support vector machines; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6609799

Filename

6609799