College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
PLoS One. 2013 Aug 5;8(8):e71191. doi: 10.1371/journal.pone.0071191. Print 2013.
The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.
心肌病是一组可遗传（家族性）的心肌疾病。鉴定潜在的与疾病相关的蛋白质对于理解心肌病的机制非常重要。实验鉴定心肌病既昂贵又费力。相比之下，生物信息学方法具有比实验方法更具竞争力的优势。基于“关联定罪”分析，我们优先考虑涉及人类心肌病的候选蛋白。我们首先使用来自在线孟德尔遗传数据库中的已知疾病蛋白作为种子，为三种心肌病亚型构建了加权人类心肌病特异性蛋白质-蛋白质相互作用网络。然后，我们开发了一种基于“关联定罪”分析对候选蛋白进行优先级排序的方法，以对网络中的候选蛋白进行排名。结果发现，大多数得分较高的候选蛋白与疾病种子蛋白共享与疾病相关的途径。这些排名靠前的候选蛋白与相应的疾病亚型相关，是潜在的与疾病相关的蛋白。交叉验证和与其他方法的比较表明，我们的方法可用于鉴定潜在的新疾病蛋白，这可能以更全面和综合的方式提供对心肌病相关机制的深入了解。
