Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

Protein complexes detection based on node local properties and gene expression in PPI weighted networks

BACKGROUND

Identifying protein complexes from protein-protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection.

RESULT

Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors.

CONCLUSIONS

Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

Protein complex prediction in interaction network based on network motif

The enormous size of Protein-Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.