Co-regulatory Functional Module Detection based on Affinity Propagation and Neighborhood Inflation
首发时间:2019-04-15
Abstract:At co-regulatory level, transcription factors (TFs) and microRNAs (miRNAs) co-regulate the gene expression, and the co-regulatory functional module (CRM) combined by these three components can serve as building blocks of co-regulatory network which could cooperatively participate in post-transcriptional level. Identification of CRMs can help comprehend regulatory mechanism of complex diseases and reveal pathogenesis. However, recent researches about detecting regulatory modules involving a single kind of regulators might not completely bring to light complex regulatory mechanism. In this paper, we propose a novel computational framework called APNICRM which detects CRMs by affinity propagation (AP) and neighborhood inflation (NI). APNICRM firstly utilizes regularized least squares to construct a co-regulatory network from multiple sources of data: miRNA/TF/mRNA expression profiles and priori regulatory relationships (miRNA-mRNA/TF-miRNA/TF-mRNA). Then, in order to produce meaningful seeds for neighborhood inflation, affinity propagation clustering and modified neighborhood similarity will be adopted. To further infer CRMs, we develop new greedily expanding strategies that optimize internal clustering fitness fuction. In addition, for improving the speed of operation, multi-threaded approach is executed for paralleling. Results on the Ovarian Cancer (OV) dataset and Breast Cancer (BRCA) have shown that our proposed method is able to produce compact and meaningful functional modules that are highly relevant to the biological conditions. Comparing with existing methods, CRMs of APNICRM include more significant biological function GO-BP terms and KEGG pathways with high enrichment scores. In addition, topological characteristics analysis and case studies verify the effectiveness.
keywords: bioinformatics co-regulatory network overlapping functional modules affinity propagation neighborhood inflation
点击查看论文中文信息
基于亲和传播和领域扩散的功能模块识别算法
摘要:转录因子(TF)和microRNA(miRNA)共同调节基因表达,发生扰动可导致人体系统故障和疾病发生。因此,识别miRNA-TF-mRNA共调控模块(CRM)可以帮助理解复杂疾病发病机制。最近,已有一些计算方法用来识别调控功能模块,但很少有涉及到转录因子层面。然而,仅仅涉及单一调控子的模块可能无法完全揭示复杂疾病的调控机制。在本文中,我们提出了一种新的计算框架APNICRM,它通过亲和传播(AP)和邻域膨胀(NI)来检测CRM。 APNICRM 首先使用正则化最小二乘法利用表达谱数据和先验关系构建共调控网络。然后,采用亲和传播聚类和邻域相似性确定种子集合。为了进一步推断CRM,我们提出了新的适应度函数,贪婪地扩展模块直到达到最优适应度。并且为了提高运行速度,我们以多线程的方式执行程序。卵巢癌(OV)和乳腺癌(BRCA)数据集的结果表明,我们提出的方法能够挖掘到紧凑且有生物意义的功能模块。此外,拓扑特征分析和案例研究验证了有效性。APNICRM为从网络水平上研究癌症致病机制提供了新的视角。
引用
No.****
同行评议
勘误表
基于亲和传播和领域扩散的功能模块识别算法
评论
全部评论0/1000