PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
复制标题
PBMDA：一种新颖有效的基于路径的 miRNA 疾病关联预测计算模型
DOI：
10.1371/journal.pcbi.1005455
复制
发表时间：
2017-03
影响因子：
4.3
通讯作者：
Chen X
中科院分区：
文献类型：
--
作者：
You ZH;Huang ZA;Zhu Z;Yan GY;Li ZW;Wen Z;Chen X
近年来，越来越多的研究表明microRNA（miRNAs）在许多基础和重要的生物学过程中发挥着关键作用。作为人类复杂疾病的致病因素之一，miRNA的分子机制尚未完全阐明。预测潜在的miRNA与疾病的关联对于了解疾病的发病机制、开发新药以及制定针对多种人类复杂疾病的个性化诊断和治疗做出了重要贡献。而不是仅仅依赖于昂贵和耗时的生物学实验，计算预测模型是有效的，通过预测潜在的miRNA-疾病的关联，优先考虑候选miRNA的研究疾病，并选择那些具有更高的关联概率的miRNA进行进一步的实验验证。本研究通过整合已知的人类miRNA与疾病的关联、miRNA功能相似性、疾病语义相似性以及miRNA与疾病的高斯相互作用谱核相似性，提出了基于路径的miRNA与疾病关联（Path-Based MiRNA-Disease Association，PBMDA）预测模型。该模型构建了一个由三个相互连接的子图组成的异构图，并进一步采用深度优先搜索算法来推断潜在的miRNA-疾病关联。因此，PBMDA在局部和全局LOOCV（AUC分别为0.8341和0.9169）和5倍交叉验证（平均AUC为0.9172）的框架中均实现了可靠的性能。在三种重要的人类疾病的案例研究中，88%（食管肿瘤）、88%（肾肿瘤）和90%（结肠肿瘤）的前50个预测的miRNAs已经被文献中的先前实验报告手动确认。通过实例分析比较PBMDA与其他模型的性能，其可靠的性能也表明PBMDA可以作为一个强大的计算工具，加速疾病-miRNA关联的识别。
In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.