UDoGeC:Essential Protein Prediction Using Domain and Gene Expression Profiles

Shabnam, C.B. Fathima; Izudheen, Sminu

doi:10.1016/j.procs.2016.07.300

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…So far, based on above equations (17), (19) and 22, we can obtain a new distribution rate matrix DRM as follows:…”

Section: F Construction Of (K+n)×(k+n) Dimensional Distribution Ratementioning

confidence: 99%

“…Y Fan et al [18] took advantage of the subcellular localization and Person correlation coefficient to construct a new weighted PPI network to identify essential proteins. Shabnam and Izudheen [19] proposed a novel prediction model by integrating both gene expression profile and domain information to infer potential key proteins based on the hypothesis that key proteins are inclined to form dense cluster. W Zhang et al [15] incorporated three kinds of data such as the gene expression data, the Go annotation data, and the topological feature data to calculate the essentiality of proteins.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Model for Predicting Essential Proteins Based on Heterogeneous Protein-Domain Network

et al. 2020

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Essential proteins play significant roles in cell survive. In current years, some Protein-Protein Interaction (PPI) data have been discovered in saccharomyces cerevisiae. Due to the high costs of biological experiments, a growing number of computational models are adopted to predict essential proteins. However, the identification accuracy of these computational models still has broad space for improvement. In this paper, a novel prediction model called NPRI is proposed to infer potential essential proteins based on the PageRank algorithm. In NPRI, a new heterogeneous Protein-Domain network will be constructed by integrating three kinds of networks such as the weighted PPI network, the Domain-Domain network and the initial Protein-Domain network first. Here, these three kinds of networks are established in accordance with gene expression data, original PPI network and known Protein-Domain network respectively. Next, based on the newly constructed heterogeneous Protein-Domain network, we will extract functional features and topological characteristics for each protein to further construct a novel distribution rate network. And then, an improved iteration method based on the PageRank algorithm will be implemented on the novel distribution rate network to infer essential proteins. Finally, in order to evaluate the performance of NPRI, we will compare NPRI with other state-of-the-art prediction models, and simulation results show that NPRI can achieve reliable identification accuracies of 90%, 84.5% and 79% in top 100, 200 and 300 predicted candidate essential proteins separately, which outperform these competitive models remarkably, and means that NPRI is a promising framework for identifying essential proteins as well. INDEX TERMS Essential proteins, protein-protein interaction network, domain-domain network, heterogeneous protein-domain network, pagerank algorithm.

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“…So far, based on above equations (17), (19) and 22, we can obtain a new distribution rate matrix DRM as follows:…”

Section: F Construction Of (K+n)×(k+n) Dimensional Distribution Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Model for Predicting Essential Proteins Based on Heterogeneous Protein-Domain Network

et al. 2020

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“…Lei et al [14] proposed a computational model RSG, which identifies key proteins based on a novel weighted PPI network constructed based on information from RNA-Seq, subcellular localization, and GO annotation data sets. Based on the assumption that key proteins tend to form dense clusters, Shabnam and Izudheen [15] constructed a prediction model by integrating gene expression profiles and domain information. Zhao et al [16] constructed a weighted network based on gene expression data and topological information of the weighted network, and designed a calculation method named POEM based on this network to predict key proteins based on overlapping modules.…”

Section: Introductionmentioning

confidence: 99%

A Novel Network-Based Computational Model for Prediction of Essential Proteins

et al. 2020

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Identification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and inefficient, more and more computational models are proposed for predicting essential proteins in recent years. In this paper, a novel computational model called BSPM is proposed, in which, an original PPI network will be built based on known protein-protein associations first, and then topology information of the original PPI network will be adopted to measure the similarities between proteins based on the SimRank algorithm. Thereafter, a weighted PPI network can be obtained based on the similarities between proteins and the original PPI network. Finally, based on the weighted PPI network, the PageRank algorithm will be used to infer potential essential proteins. Moreover, in order to evaluate the performance of BSPM, we have compared the performance of BSPM with 14 classical prediction models in the field based on two different databases, and experimental results show that BSPM can achieve prediction accuracies of 92%, 81% and 76% out of the top 100, 200 and 300 candidate proteins separately, which not only are significantly better than those 14 competitive classical prediction models, but also means that BSPM can be used as an effective model for identifying essential proteins in the future.

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