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Predicting Aging-Genes in Drosophila Melanogaster by Integrating Network Topological Features and Functional Categories

Predicting Aging-Genes in Drosophila Melanogaster by Integrating Network Topological Features and Functional Categories

Yan-Hui Li, Jian-Hui Li, Xin Song, Kai Feng, Yuan-Chun Zhou
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 11
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466613225|DOI: 10.4018/jkdb.2012040102
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MLA

Li, Yan-Hui, et al. "Predicting Aging-Genes in Drosophila Melanogaster by Integrating Network Topological Features and Functional Categories." IJKDB vol.3, no.2 2012: pp.16-26. http://doi.org/10.4018/jkdb.2012040102

APA

Li, Y., Li, J., Song, X., Feng, K., & Zhou, Y. (2012). Predicting Aging-Genes in Drosophila Melanogaster by Integrating Network Topological Features and Functional Categories. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 3(2), 16-26. http://doi.org/10.4018/jkdb.2012040102

Chicago

Li, Yan-Hui, et al. "Predicting Aging-Genes in Drosophila Melanogaster by Integrating Network Topological Features and Functional Categories," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 3, no.2: 16-26. http://doi.org/10.4018/jkdb.2012040102

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Abstract

An important task of aging research is to find genes that regulate lifespan. Wet-lab identification of aging genes is tedious and labor-intensive activity. Developing an algorithm to predict aging genes will be greatly helpful. In this paper, we systematically analyzed topological features of proteins encoded by Drosophila melanogaster aging genes versus those encoded by non-aging genes in protein-protein interaction (PPI) network and found that aging genes are characterized by several network topological features such as higher in degrees. And aging genes tend to be enriched in certain functions were also found. Based on these features, an algorithm was developed to detect aging genes genome wide. With a posterior probability score describing possible involvement in aging no less than 1, 1014 novel aging genes were predicted by decision trees. Evidence supporting our prediction can be found.

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