School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Institute for Biomedical Informatics, University of Kentucky, Lexington, USA; Department of Internal Medicine, University of Kentucky, Lexington, USA; Department of Computer Science, University of Kentucky, Lexington, USA.
Methods. 2017 Dec 1;131:104-110. doi: 10.1016/j.ymeth.2017.07.024. Epub 2017 Aug 12.
It is critical to identify disease-specific subnetworks from the vastly available genome-wide gene expression data for elucidating how genes perform high-level biological functions together. Various algorithms have been developed for disease gene identification. However, the topological structure of the disease networks (or even the fraction of the networks) has been left largely unexplored. In this article, we present DNet, a method for the identification of significant disease subnetworks by integrating both the network structure and gene expression information. Our work will lead to the identification of missing key disease genes, which are be highly expressed in a disease-specific gene expression dataset. The experimental evaluation of our method on both the Leukemia and the Duchenne Muscular Dystrophy gene expression datasets show that DNet performs better than the existing state-of-the-art methods. In addition, literature supports were found for the discovered disease subnetworks in a case study.
从广泛可用的全基因组基因表达数据中识别特定于疾病的子网对于阐明基因如何共同执行高级生物学功能至关重要。已经开发了各种用于疾病基因识别的算法。然而，疾病网络的拓扑结构（甚至网络的一部分）在很大程度上仍未得到探索。在本文中，我们提出了 DNet，这是一种通过整合网络结构和基因表达信息来识别重要疾病子网的方法。我们的工作将导致识别缺失的关键疾病基因，这些基因在特定疾病的基因表达数据集中高度表达。我们的方法在白血病和杜氏肌营养不良症基因表达数据集上的实验评估表明，DNet 比现有的最先进方法表现更好。此外，在案例研究中发现了所发现的疾病子网的文献支持。
