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Title: Meta-analysis of genomic and proteomic features to predict synthetic lethality of yeast and human cancer
Authors: Wu, Min
Li, Xuejuan
Zhang, Fan
Li, Xiaoli
Kwoh, Chee Keong
Zheng, Jie
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2013
Source: Wu, M., Li, X., Zhang, F., Li, X., Kwoh, C. K.,& Zheng, J. (2007). Meta-analysis of genomic and proteomic features to predict synthetic lethality of yeast and human cancer. Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics - BCB'13.
Abstract: A major goal in cancer medicine is to find selective drugs with reduced side-effect. A pair of genes is called synthetic lethality (SL) if mutations of both genes will kill a cell while mutation of either gene alone will not. Hence, a gene in SL interactions with a cancer-specific mutated gene will be a promising drug target with anti-cancer selectivity. Wet-lab screening approach is still so costly that even for yeast only a small fraction of gene pairs has been covered. Computational methods are therefore important for large-scale discovery of SL interactions. Most existing approaches focus on individual features or machine learning methods, which are prone to noise or overfitting. In this paper, we propose an approach of meta-analysis that integrates 17 genomic and proteomic features and the outputs of 10 classification methods. It thus combines the strengths of existing methods. It also adjusts relative contributions of multiple methods with weights learned from the training data. Running on a dataset of the yeast strain of S. cerevisiae, our method achieves AUC (area under ROC curve) of 87.2% the highest among all competitors. Moreover, through orthologous mapping from yeast to human genes, we predicted a list of SL pairs in human that contain top mutated genes in lung and breast cancers recently reported by The Cancer Genome Atlas (TCGA). Our method and predictions would shed light on mechanisms of SL and lead to discovery of novel anti-cancer drugs.
DOI: 10.1145/2506583.2506653
Rights: © 2013 ACM, Inc. This is the author created version of a work that has been peer reviewed and accepted for publication by The International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, BCB'13, ACM, Inc. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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