Predicting Yeast Synthetic Lethal Genetic Interactions Using Protein Domains

Author

Li, Bo ; Luo, Feng

Author_Institution

Sch. of Comput., Clemson Univ., Clemson, SC, USA

fYear

2009

fDate

1-4 Nov. 2009

Firstpage

43

Lastpage

47

Abstract

Synthetic lethal genetic interactions are of interest as they can be used to predict function of unknown proteins and find drug target or drug combinations. In this study, we applied support vector machine (SVM) classifier to predict synthetic lethal genetic interactions in Saccharomyces cerevisiae based on domain information in proteins. We found that our method can predict synthetic lethal genetic interactions with high sensitivity (88.35%) and specificity (82.00%). To the best of our knowledge, the work reported in this paper is the first domain-based model for the prediction of genetic interactions. Our study indicates that there is strong correlation between protein domain relationship and synthetic lethal genetic interactions.

Keywords

biology computing; genetics; molecular biophysics; proteins; support vector machines; Saccharomyces cerevisiae; protein domains; support vector machine classifier; yeast synthetic lethal genetic interactions; Bioinformatics; Databases; Drugs; Fungi; Genetic mutations; Genomics; Predictive models; Proteins; Support vector machine classification; Support vector machines; Genetic interactions; SVM; prediction; protein domains;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine, 2009. BIBM '09. IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

978-0-7695-3885-3

Type

conf

DOI

10.1109/BIBM.2009.37

Filename

5341871