Predicting essential proteins by integrating orthology, gene expressions, and PPI networks

Xue, Zhang; Xiao, Wangxin; Hu, Xihao

doi:10.1371/journal.pone.0195410

Cited by 28 publications

(25 citation statements)

References 32 publications

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“…Zhang et al proposed an ensemble framework based on gene expression data and PPI networks, which can significantly improve the prediction accuracy of common used centrality measures [2]. Zhang et al also proposed an integrated method, OGN, by combining network topological properties, the probability of co-expression with the neighboring proteins, and the orthologs in reference organisms [3]. Li et al proposed GOS [4] by integrating gene expression, orthology, subcellular localization and PPI networks to predict gene essentiality.…”

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confidence: 99%

DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning

Zhang

Xiao

2020

Preprint

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Motivation: Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. Results:We proposed a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method was utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features were integrated to train a multilayer neural network. A cost-sensitive technique was used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes showed that our proposed method, DeepHE, can accurately predict human gene essentiality with an average AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compared DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, Adaboost). The experimental results showed that DeepHE greatly outperformed the compared machine learning models. Conclusions:We demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task.Essential genes are a subset of genes which are indispensable to the survival or reproduction of a living organism. The prediction of gene essentiality is very important for understanding the minimal requirements of an organism, identifying disease genes, and finding new drug targets. The discovery of essential genes via wet-lab experimental methods are often time-consuming, laborious, and costly. With the accumulation of gene essentiality data in some model organisms and human cell lines, many computational methods have been proposed to predict essential genes by exploring the correlations between gene essentiality and all sorts of biological information.One focus in this direction is network based centrality measures. Many studies have demonstrated that highly connected proteins in a protein-protein interaction (PPI) network are more likely to be esse...

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DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning

Zhang

Xiao

2020

Preprint

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“…Essential proteins exert vital roles in cellular processes and are indispensable for survival or reproduction [29,40]. Essential proteins are also critical for the development of human diseases [41]. Here, the topological features of the PPI network were used to detect the role of essential proteins in inguinal hernia disease.…”

Section: Detecting Essential Proteinsmentioning

confidence: 99%

A network analysis revealed the essential and common downstream proteins related to inguinal hernia

Mao

Chen

et al. 2020

PLoS ONE

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Although more than 1 in 4 men develop symptomatic inguinal hernia during their lifetime, the molecular mechanism behind inguinal hernia remains unknown. Here, we explored the protein-protein interaction network built on known inguinal hernia-causative genes to identify essential and common downstream proteins for inguinal hernia formation. We discovered that PIK3R1, PTPN11, TGFBR1, CDC42, SOS1, and KRAS were the most essential inguinal hernia-causative proteins and UBC, GRB2, CTNNB1, HSP90AA1, CBL, PLCG1, and CRK were listed as the most commonly-involved downstream proteins. In addition, the transmembrane receptor protein tyrosine kinase signaling pathway was the most frequently found inguinal hernia-related pathway. Our in silico approach was able to uncover a novel molecular mechanism underlying inguinal hernia formation by identifying inguinal herniarelated essential proteins and potential common downstream proteins of inguinal herniacausative proteins. OPEN ACCESSCitation: Mao Y, Chen L, Li J, Shangguan AJ, Kujawa S, Zhao H (2020) A network analysis revealed the essential and common downstream proteins related to inguinal hernia. PLoS ONE 15 (1): e0226885. https://doi.org/10.

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“…They used a birelational graph to propagate from annotated to unannotated proteins. Recently, OGN (Zhang et al, 2018), a centrality measure-based method, has been proposed. It integrates orthologous information, gene expression, and protein interaction data to predict essential proteins.…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Wekesa

Luan

Meng

2021

Journal of Computational Biology

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Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

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Predicting essential proteins by integrating orthology, gene expressions, and PPI networks

Cited by 28 publications

References 32 publications

DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning

DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning

A network analysis revealed the essential and common downstream proteins related to inguinal hernia

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

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