A new computational strategy for identifying essential proteins based on network topological properties and biological information

Abstract

Essential proteins are the proteins that are indispensable to the survival and development of an organism. Deleting a single essential protein will cause lethality or infertility. Identifying and analysing essential proteins are key to understanding the molecular mechanisms of living cells. There are two types of methods for predicting essential proteins: experimental methods, which require considerable time and resources, and computational methods, which overcome the shortcomings of experimental methods. However, the prediction accuracy of computational methods for essential proteins requires further improvement. In this paper, we propose a new computational strategy named CoTB for identifying essential proteins based on a combination of topological properties, subcellular localization information and orthologous protein information. First, we introduce several topological properties of the protein-protein interaction (PPI) network. Second, we propose new methods for measuring orthologous information and subcellular localization and a new computational strategy that uses a random forest prediction model to obtain a probability score for the proteins being essential. Finally, we conduct experiments on four different Saccharomyces cerevisiae datasets. The experimental results demonstrate that our strategy for identifying essential proteins outperforms traditional computational methods and the most recently developed method, SON. In particular, our strategy improves the prediction accuracy to 89, 78, 79, and 85 percent on the YDIP, YMIPS, YMBD and YHQ datasets at the top 100 level, respectively.

Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy

“…The key advantage of these strategies lies in the fact that these models are capable of capturing the inherent patterns of a large array of biologically relevant 'features' that are distinctive and reflect the heterogeneous properties of essential genes. Supervised machine learning classifiers such as logistic regression [26,27], support vector machine [28][29][30][31], random forest [32], decision tree [26], ensemble [26] and probabilistic Bayesian-based…”

“…The key advantage of these strategies lies in the fact that these models are capable of capturing the inherent patterns of a large array of biologically relevant ‘features’ that are distinctive and reflect the heterogeneous properties of essential genes. Supervised machine learning classifiers such as logistic regression [ 26 , 27 ], support vector machine [ 28 – 31 ], random forest [ 32 ], decision tree [ 26 ], ensemble [ 26 ] and probabilistic Bayesian-based methods [ 26 , 27 , 33 ] and instance-based learning methods such as K Nearest neighbor (K-NN) and Weighted KNN (WKNN) [ 34 ] have been used for gene essentiality prediction. Deep Learning strategies based on multilayer perceptron networks have also been used for essential gene prediction [ 24 , 35 ].…”

Essential Protein Prediction Based on Shuffled Frog‐Leaping Algorithm

“…Previous studies suggest that a protein's conservation is related to its essentiality. The larger the number of reference organisms where a protein has othologs, the more essential the protein is [ 20 , 21 ] . Given a reference species , let be the set of proteins which appear in the th reference species, be the number of times that protein appears in the reference organisms, and is the number of reference organisms.…”

A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins

“…Y. Fan et al (2016) proposed a novel prediction model by adopting Pearson correlation coefficients and subcellular localization to update the PPI network Qin et al (2017) put forward a method for recognizing essential proteins based on the topological information of PPI networks and orthologous information of proteins. Peng et al (2012) proposed an advanced iterative algorithm named ION for identifying key proteins based on the topological information of PPI networks and homologous information of proteins.…”