Construction of dynamic protein interaction network based on gene expression data and quartile one principle
Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low‐ or high‐expression proteins as active proteins....
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| Published in | Proteins, structure, function, and bioinformatics Vol. 90; no. 5; pp. 1219 - 1228 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.05.2022
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0887-3585 1097-0134 1097-0134 |
| DOI | 10.1002/prot.26304 |
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| Abstract | Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low‐ or high‐expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q‐one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q‐one approach and three‐sigma (3‐sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q‐one outperforms the 3‐sigma method in recall, precision and F‐measure. This is in addition to its ability to reveal the dynamicity within the protein–protein interaction network and identify essential proteins. |
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| AbstractList | Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low‐ or high‐expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q‐one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q‐one approach and three‐sigma (3‐sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q‐one outperforms the 3‐sigma method in recall, precision and F‐measure. This is in addition to its ability to reveal the dynamicity within the protein–protein interaction network and identify essential proteins. Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low- or high-expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q-one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q-one approach and three-sigma (3-sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q-one outperforms the 3-sigma method in recall, precision and F-measure. This is in addition to its ability to reveal the dynamicity within the protein-protein interaction network and identify essential proteins.Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low- or high-expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q-one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q-one approach and three-sigma (3-sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q-one outperforms the 3-sigma method in recall, precision and F-measure. This is in addition to its ability to reveal the dynamicity within the protein-protein interaction network and identify essential proteins. |
| Author | Al‐Shamery, Eman Noori, Soheir Al‐A'araji, Nabeel |
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| Cites_doi | 10.1126/science.1120499 10.1007/s11704-020-8179-0 10.1101/gr.1239303 10.1093/nar/gkn858 10.1186/1471-2105-15-335 10.1038/nature03607 10.1186/1471-2105-12-339 10.1089/cmb.2008.01TT 10.1093/bib/bbp057 10.1101/gad.1450606 10.1186/1471-2105-10-99 10.1073/pnas.2032324100 10.1089/cmb.2017.0114 10.1093/nar/30.1.69 10.1186/s12866-020-01904-6 10.1093/nar/gkj148 10.1002/pmic.201300257 10.1007/s10044-017-0626-7 10.1038/35001009 10.1093/nar/gkn1005 10.1109/BIBM.2011.45 10.1038/nmeth.1938 10.1186/s12859-016-1054-1 10.1155/2017/4120506 10.1093/bioinformatics/btp668 10.1093/nar/30.1.303 10.1002/pmic.201200277 10.1142/S0219720015710018 10.1126/science.1105103 10.1142/S0219720019500252 10.1186/s12859-018-2309-9 10.1186/s12859-021-04175-8 10.3389/fgene.2020.00567 10.1534/genetics.114.161620 |
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| Keywords | quartile one principle gene expression profile protein complexes dynamic protein interaction network protein activity |
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| SubjectTerms | Algorithms Computational Biology - methods dynamic protein interaction network Gene Expression gene expression profile Percolation protein activity protein complexes Protein interaction Protein Interaction Mapping - methods Protein Interaction Maps - genetics Proteins Proteins - genetics Proteins - metabolism quartile one principle |
| Title | Construction of dynamic protein interaction network based on gene expression data and quartile one principle |
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