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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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 37; H. 16; S. 2423 - 2431
Hauptverfasser: Zhang, Yuelei, Chang, Xiao, Liu, Xiaoping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 25.08.2021
Oxford Publishing Limited (England)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btab099