Predicting disease-related genes using integrated biomedical networks.
Predicting disease-related genes using integrated biomedical networks.
复制标题
使用集成生物医学网络预测疾病相关基因
DOI：
10.1186/s12864-016-3263-4
复制
发表时间：
2017-01-25
期刊：
影响因子：
4.4
通讯作者：
Chen J
中科院分区：
文献类型：
--
作者：
Peng J;Bai K;Shang X;Wang G;Xue H;Jin S;Cheng L;Wang Y;Chen J
背景识别与人类疾病相关的基因对于疾病诊断和药物设计至关重要。近年来，计算方法，特别是基于网络的方法，已经发展到从现有的生物医学网络中有效地识别疾病相关基因。与此同时，生物技术的进步使研究人员能够产生多组学数据，丰富了我们对人类疾病的理解，并揭示了基因与疾病之间的复杂关系。然而，在这方面，现有的计算方法都不能将大量的组学数据集成到一个加权的集成网络中，并利用它来增强疾病相关基因的发现。结果我们提出了一种新的基于网络的疾病基因预测方法SLN-SRW（简化拉普拉斯归一化-监督随机游走）来生成和建模新的生物医学网络的边权重，该网络集成了来自异构源的生物医学数据，结论实验结果表明，SLN-SRW在真实的数据集和人工合成数据集上均显著提高了疾病基因预测的性能。
BackgroundIdentifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.ResultsWe propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.ConclusionsThe experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.