Chen Lina, Tai Jingxie, Zhang Liangcai, Shang Yukui, Li Xu, Qu Xiaoli, Li Weiguo, Miao Zhengqiang, Jia Xu, Wang Hong, Li Wan, He Weiming
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China.
Mol Biosyst. 2011 Sep;7(9):2547-53. doi: 10.1039/c1mb05134b. Epub 2011 Jul 7.
Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.
精确识别致病基因有助于理解复杂疾病的发病机制。人们已经开发了许多计算方法来对候选疾病基因进行优先级排序，但功能注释的覆盖范围可能是这些方法中的大多数的限制因素。在这里，我们引入了一种全局候选基因优先级排序方法，该方法考虑了人类蛋白质相互作用网络中的网络属性信息以及已知疾病基因的风险转化内容。然后使用全局风险转化分数对候选基因进行优先级排序。该方法被引入用于对前列腺癌的候选基因进行优先级排序。根据验证研究评估了我们的全局风险转化算法对候选基因进行优先级排序的有效性。与ToppGene和基于随机游走的方法相比，我们的方法优于其他两种候选基因优先级排序方法。通过在前列腺癌和其他类型癌症上进行测试来评估我们方法的通用性。使用标准的留一法交叉验证来评估性能。
