Abstract：
The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.
摘要：
功能丰富且具有生物学能力的双聚类算法的开发对于从海量生物数据集中提取隐藏信息至关重要。本文提出了一种新颖的基于p值的双串合奏，称为EnsemBic，计算遗传关联的功能相似性。为了验证EnsemBic的有效性和鲁棒性，我们应用了三种众所周知的双层建筑技术，viz.LaplacePrior,iBBiG,和xMotif来实现EnsemBic，并使用不同的领先参数进行了比较。据观察，EnsemBic在几种突出的功能和生物学措施中优于其竞争算法。接下来,从EnsemBic获得的双簇用于通过参考精英基因探索拓扑和生物学相关性来鉴定食管鳞状细胞癌（ESCC）的潜在生物标志物，从基因卡获得。最后,我们发现F2RL3,APPL1,CALM1,IFNGR1,LPAR1,ANGPT2,ARPC2,CGN,CLDN7,ATP6V1C2,CEACAM1,FTL,部队,PSMB4和EPHB2携带先前建立的ESCC精英基因的拓扑和生物学意义。因此,我们宣布上述基因为ESCC的潜在生物标志物。
