Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks

Abstract

Module identification is a frequently used approach for mining local structures with more significance in global networks. Recently, a wide variety of bilayer networks are emerging to characterize the more complex biological processes. In the light of special topological properties of bilayer networks and the accompanying challenges, there is yet no effective method aiming at bilayer module identification to probe the modular organizations from the more inspiring bilayer networks. To this end, we proposed the pseudo-3D clustering algorithm, which starts from extracting initial non-hierarchically organized modules and then iteratively deciphers the hierarchical organization of modules according to a bottom-up strategy. Specifically, a modularity function for bilayer modules was proposed to facilitate the algorithm reporting the optimal partition that gives the most accurate characterization of the bilayer network. Simulation studies demonstrated its robustness and outperformance against alternative competing methods. Specific applications to both the soybean and human miRNA-gene bilayer networks demonstrated that the pseudo-3D clustering algorithm successfully identified the overlapping, hierarchically organized and highly cohesive bilayer modules. The analyses on topology, functional and human disease enrichment and the bilayer subnetwork involved in soybean fat biosynthesis provided both the theoretical and biological evidence supporting the effectiveness and robustness of pseudo-3D clustering algorithm.

BRWLDA: bi-random walks for predicting lncRNA-disease associations

“…It has been proven that some lncRNAs act as competing endogenous RNAs in the regulation of gene expression [ 69 ]. The functional interactions between miRNAs and lncRNAs, and the crucial roles of miRNAs in various biological processes (including the affinity with genetic transcription and diseases) [ 70 , 71 ] drive us to measure the lncRNA similarity based on lncRNA-miRNA associations. The similarity between two miRNAs ( m 1 and m 2 ) is defined as follows: where D ( m 1 ) and D ( m 2 ) indicate the disease sets related to m 1 and m 2 , respectively.…”

Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods

“…The greater the distance between features, the less redundancy exists in the vectors. The final feature set created by this method has less redundancy and greater correlation with the target set (Xu et al, 2016, 2018; Jiang et al, 2017; Wei et al, 2017c).…”

Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers

“…It aims to select features with maximum relevance and maximum distance. It uses Pearson's correlation coefficient to measure the correlation of feature and label, and uses Euclidean distance between features to calculate redundancy, which was also widely used in clustering [58][59][60]. MRMD ranks all candidate features based on the calculated Pearson's correlation coefficient and Euclidean distance, then constructs a simple classifier using the top-ranked features, and finally selects a feature list with the best classification accuracy.…”