Title :
Predicting Yeast Synthetic Lethal Genetic Interactions Using Protein Domains
Author :
Li, Bo ; Luo, Feng
Author_Institution :
Sch. of Comput., Clemson Univ., Clemson, SC, USA
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;
Conference_Titel :
Bioinformatics and Biomedicine, 2009. BIBM '09. IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-0-7695-3885-3
DOI :
10.1109/BIBM.2009.37
