Mazza Arnon, Klockmeier Konrad, Wanker Erich, Sharan Roded
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Max Delbrück Center for Molecular Medicine, Berlin, Germany.
Bioinformatics. 2016 Jun 15;32(12):i271-i277. doi: 10.1093/bioinformatics/btw263.
Unraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process.
Here, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge.
The datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zip
揭示疾病背后的分子机制需要超越识别单个致病基因的方法，以推断参与疾病过程的更大蛋白质组装体。
在此，我们开发了一种基于整数规划的精确方法，用于将蛋白质复合物与疾病相关联。我们的方法根据蛋白质在蛋白质-蛋白质相互作用网络中与已知与所研究疾病相关的先前集合的接近程度对蛋白质进行评分。这些分数与相互作用信息相结合，以推断可能与疾病相关的紧密相互作用的蛋白质复合物。我们表明，我们的方法优于以前的方法，并得出了得到当前实验数据和文献知识充分支持的预测结果。
我们使用的数据集、可执行文件和结果可在www.cs.tau.ac.il/roded/disease_complexes.zip获取。
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