A random walk-based method for detecting essential proteins by integrating the topological and biological features of PPI network

Essential proteins discover based on hypergraph and mult-omics data integration model

Essential proteins are critical for cell regulation, reproduction and metabolism, with their absence leading to the cessation of cell replication or cell death. Therefore, identifying essential proteins is vital for the developing antibiotics, new therapies and targeted drugs. However, current computational methods rarely or insufficiently consider the impact of features importance and the noisy data in protein-protein interaction network on recognition accuracy. To address these issues, we propose a essential protein identification method based on a hypergraph and Hierarchical Advantage Suppression Model, HGMO. Firstly, based on the principles of co-expression and co-localization, a hypergraph network is constructed using protein-protein interaction network, gene expression data, and subcellular localization data. From this network, a new feature score, the Topological Significance (TS) score, is extracted. Then, we extract subcellular localization scores and orthologous scores from the subcellular localization data and orthologous data, respectively. Finally, we uses a multi-omics data integration model, Hierarchical Advantage Suppression Model, for feature fusion, thus proposing the HGMO method. Experimental results show that HGMO consistently outperforms other methods on three S.cerevisiae datasets. Moreover, TS demonstrates a higher identification rate than other centrality methods.

A Novel Method for Targeted Identification of Essential Proteins by Integrating Chemical Reaction Optimization and Naive Bayes Model

Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins. In EPCRO, the naive Bayes model is employed to analyze the homogeneity between proteins. In order to improve the identification rate and speed of essential proteins, the protein homogeneity rate is integrated into the CRO algorithm to balance between local and global searches. EPCRO is experimentally compared with 17 existing methods (including, DC, SC, IC, EC, LAC, NC, PeC, WDC, EPD-RW, RWHN, TEGS, CFMM, BSPM, AFSO-EP, CVIM, RWEP, and EPPSO-DC) based on biological datasets. The results show that EPCRO is superior to the above methods in identification accuracy and speed.

A Construction Method of Dynamic Protein Interaction Networks by Using Relevant Features of Gene Expression Data

Essential proteins play an important role in various life activities and are considered to be a vital part of the organism. Gene expression data are an important dataset to construct dynamic protein-protein interaction networks (DPIN). The existing methods for the construction of DPINs generally utilize all features (or the features in a cycle) of the gene expression data. However, the features observed from successive time points tend to be highly correlated, and thus there are some redundant and irrelevant features in the gene expression data, which will influence the quality of the constructed network and the predictive performance of essential proteins. To address this problem, we propose a construction method of DPINs by using selected relevant features rather than continuous and periodic features. We adopt an improved unsupervised feature selection method based on Laplacian algorithm to remove irrelevant and redundant features from gene expression data, then integrate the chosen relevant features into the static protein-protein interaction network (SPIN) to construct a more concise and effective DPIN (FS-DPIN). To evaluate the effectiveness of the FS-DPIN, we apply 15 network-based centrality methods on the FS-DPIN and compare the results with those on the SPIN and the existing DPINs. Then the predictive performance of the 15 centrality methods is validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife and AUPRC. The experimental results show that the FS-DPIN is superior to the existing DPINs in the identification accuracy of essential proteins.

Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network

In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.