Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models
Big data; Complex networks; Diseases; Escherichia coli; Gene expression; Least squares approximations; Multivariant analysis; Regression analysis; Computational analysis; Gene regulatory networks; Large-scale gene regulatory networks; Multi variate analysis; Partial least square (PLS); PCA (principa...
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2023
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my.uniten.dspace-248152023-05-29T15:27:26Z Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models Mohamed Salleh F.H. Zainudin S. Raih M.F. 26423229000 24479069300 57221461047 Big data; Complex networks; Diseases; Escherichia coli; Gene expression; Least squares approximations; Multivariant analysis; Regression analysis; Computational analysis; Gene regulatory networks; Large-scale gene regulatory networks; Multi variate analysis; Partial least square (PLS); PCA (principal component analysis); Regression-based model; Regulatory interactions; Principal component analysis Gene regulatory networks (GRN) reconstruction is the process of identifying gene regulatory interactions from experimental data through computational analysis. GRN reconstruction-related works have boosted many major discoveries in finding drug targets for the treatment of human diseases, including cancer. However, reconstructing GRNs from gene expression data is a challenging problem due to high-dimensionality and very limited number of observations data, severe multicollinearity and the tendency of generating cascade errors. These problems lead to the reduced performance of GRN inference methods, hence resulting in the method being unreliable for scientific usage. We propose a method called P-CALS (Principal Component Analysis and Partial Least Squares) that is derived from the combination of PCA (Principal Component Analysis) with PLS (Partial Least Squares). The performance of P-CALS is assessed to the genome-scale GRN of E. coli, S. cerevisiae and an in-silico datasets. We discovered that P-CALS achieved satisfactory results as all of the sub-networks from diverse datasets achieved AUROC values above 0.5 and gene relationships were discovered at the most complex network tested in the experiments. � 2018 IEEE. Final 2023-05-29T07:27:26Z 2023-05-29T07:27:26Z 2019 Conference Paper 10.1109/ICBDAA.2018.8629777 2-s2.0-85062777322 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062777322&doi=10.1109%2fICBDAA.2018.8629777&partnerID=40&md5=ccefc53ffe564fd4e23590320ef6c794 https://irepository.uniten.edu.my/handle/123456789/24815 8629777 129 134 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Big data; Complex networks; Diseases; Escherichia coli; Gene expression; Least squares approximations; Multivariant analysis; Regression analysis; Computational analysis; Gene regulatory networks; Large-scale gene regulatory networks; Multi variate analysis; Partial least square (PLS); PCA (principal component analysis); Regression-based model; Regulatory interactions; Principal component analysis |
| author2 |
26423229000 |
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26423229000 Mohamed Salleh F.H. Zainudin S. Raih M.F. |
| format |
Conference Paper |
| author |
Mohamed Salleh F.H. Zainudin S. Raih M.F. |
| spellingShingle |
Mohamed Salleh F.H. Zainudin S. Raih M.F. Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models |
| author_sort |
Mohamed Salleh F.H. |
| title |
Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models |
| title_short |
Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models |
| title_full |
Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models |
| title_fullStr |
Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models |
| title_full_unstemmed |
Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models |
| title_sort |
reconstruction of large-scale gene regulatory networks using regression-based models |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424251753824256 |