Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient
Bioinformatics; Correlation methods; Gaussian distribution; Gaussian noise (electronic); Genes; DREAM; Gaussian model; Gene regulatory networks; Pearson correlation coefficients; Probability and statistics; Complex networks; algorithm; biological model; biology; gene inactivation; gene regulatory ne...
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2023
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my.uniten.dspace-222122023-05-29T13:59:39Z Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient Mohamed Salleh F.H. Arif S.M. Zainudin S. Firdaus-Raih M. 26423229000 26646287700 24479069300 57221461047 Bioinformatics; Correlation methods; Gaussian distribution; Gaussian noise (electronic); Genes; DREAM; Gaussian model; Gene regulatory networks; Pearson correlation coefficients; Probability and statistics; Complex networks; algorithm; biological model; biology; gene inactivation; gene regulatory network; genetics; human; Algorithms; Computational Biology; Gene Knockout Techniques; Gene Regulatory Networks; Humans; Models, Genetic A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. � 2015 Elsevier Ltd. All rights reserved. Final 2023-05-29T05:59:39Z 2023-05-29T05:59:39Z 2015 Article 10.1016/j.compbiolchem.2015.04.012 2-s2.0-84939857009 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939857009&doi=10.1016%2fj.compbiolchem.2015.04.012&partnerID=40&md5=78b3c6b08f544453c9465e147acd172b https://irepository.uniten.edu.my/handle/123456789/22212 59 3 14 Elsevier Ltd Scopus |
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| description |
Bioinformatics; Correlation methods; Gaussian distribution; Gaussian noise (electronic); Genes; DREAM; Gaussian model; Gene regulatory networks; Pearson correlation coefficients; Probability and statistics; Complex networks; algorithm; biological model; biology; gene inactivation; gene regulatory network; genetics; human; Algorithms; Computational Biology; Gene Knockout Techniques; Gene Regulatory Networks; Humans; Models, Genetic |
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26423229000 |
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26423229000 Mohamed Salleh F.H. Arif S.M. Zainudin S. Firdaus-Raih M. |
| format |
Article |
| author |
Mohamed Salleh F.H. Arif S.M. Zainudin S. Firdaus-Raih M. |
| spellingShingle |
Mohamed Salleh F.H. Arif S.M. Zainudin S. Firdaus-Raih M. Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
| author_sort |
Mohamed Salleh F.H. |
| title |
Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
| title_short |
Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
| title_full |
Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
| title_fullStr |
Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
| title_full_unstemmed |
Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient |
| title_sort |
reconstructing gene regulatory networks from knock-out data using gaussian noise model and pearson correlation coefficient |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806426630420168704 |