Inference of gene regulatory networks using pseudo-time series data

Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish...

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Vydané v:	Bioinformatics (Oxford, England) Ročník 37; číslo 16; s. 2423 - 2431
Hlavní autori:	Zhang, Yuelei, Chang, Xiao, Liu, Xiaoping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 25.08.2021 Oxford Publishing Limited (England)
Predmet:	Algorithms Bioinformatics Biological effects Cancer Datasets Gene expression Hepatocellular carcinoma Inference Liver cancer Networks Regression analysis Time series Urological cancer Urothelial cancer Urothelial carcinoma
ISSN:	1367-4803, 1367-4811, 1367-4811
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Abstract	Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availabilityand implementation The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.
AbstractList	Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary data are available at Bioinformatics online. Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks.MOTIVATIONInferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks.Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods.RESULTSHere, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods.The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR.AVAILABILITYAND IMPLEMENTATIONThe GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availabilityand implementation The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online. Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availabilityand implementation The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.
Author	Zhang, Yuelei Chang, Xiao Liu, Xiaoping
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Snippet	Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although... Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods... Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous...
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SubjectTerms	Algorithms Bioinformatics Biological effects Cancer Datasets Gene expression Hepatocellular carcinoma Inference Liver cancer Networks Regression analysis Time series Urological cancer Urothelial cancer Urothelial carcinoma
Title	Inference of gene regulatory networks using pseudo-time series data
URI	https://www.ncbi.nlm.nih.gov/pubmed/33576787 https://www.proquest.com/docview/3128005555 https://www.proquest.com/docview/2489252208
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