A common neighbor based technique to detect protein complexes in PPI networks

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.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

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.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

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.

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastasis stage, clinical stage and overall survival rate (P<0.05). There were 419 differentially expressed genes (DEGs) based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival rate (P<0.05). The biological functions of these DEGs were primarily enriched in the 'immune response' and 'regulation of cell migration and proliferation'. These DEGs were observed in a protein-protein interaction network. A LASSO Cox regression model was used to build a prognostic 6 gene-based classifier, including the IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2 genes [area under the curve (AUC) =0.776]. The predictive model which combined this classifier with clinical prognostic factors had a high accuracy in predicting patient survival in RCC (combined AUC =0.899). Taken together, these results demonstrated that there are significant associations between immune/stromal scores and clinicopathological staging. A set of tumor microenvironment-associated genes that have powerful prognostic value in patients with RCC were identified in the present study.