Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions
Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions
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DOI：
10.1101/gr.077693.108
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发表时间：
2008-12-01
期刊：
影响因子：
7
通讯作者：
Bader, Joel S.
中科院分区：
文献类型：
--
作者：
Qi, Yan;Suhail, Yasir;Bader, Joel S.
酵母合成致死遗传相互作用网络包含有关潜在途径和蛋白质复合物以及尚未发现的新遗传相互作用的丰富信息。我们开发了一个图扩散内核作为统一框架，用于从遗传相互作用网络中推断类似于“朋友”的复杂/路径成员资格和类似于“敌人”的遗传相互作用。当应用于酿酒酵母合成致死遗传相互作用网络时，我们可以在实验验证的支持下，在新遗传相互作用的全基因组预测中实现约 50% 的精确度和 20% 至 50% 的召回率。与之前从遗传相互作用数据预测遗传相互作用和蛋白质复合物成员资格的最佳方法相比，这些内核显示出显着的改进。
The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to "friends" and genetic interactions analogous to "enemies" from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data.