Inferring Boolean networks with perturbation from sparse gene expression data: a general model applied to the interferon regulatory network

2008 ◽  
Vol 4 (10) ◽  
pp. 1024 ◽  
Author(s):  
Le Yu ◽  
Steven Watterson ◽  
Stephen Marshall ◽  
Peter Ghazal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
General Model ◽  
Regulatory Network ◽  
Boolean Networks ◽  
Expression Data

scholarly journals Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

2016 ◽  
Vol 7 ◽  
Author(s):  
José P. Faria ◽  
Ross Overbeek ◽  
Ronald C. Taylor ◽  
Neal Conrad ◽  
Veronika Vonstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bacillus Subtilis ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Expression Data

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks

2012 ◽  
Vol 9 (2) ◽  
pp. 487-498 ◽  
Author(s):  
M. Hopfensitz ◽  
C. Mussel ◽  
C. Wawra ◽  
M. Maucher ◽  
M. Kuhl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Boolean Networks ◽  
Expression Data

Prediction of key transcription factors during skin regeneration by combining gene expression data and regulatory network information analysis

2019 ◽  
Vol 12 (03) ◽  
pp. 1950024
Author(s):  
Ping Huang ◽  
Peng Ge ◽  
Qing-Fen Tian ◽  
Guo-Bao Huang
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Skin Regeneration ◽  
Local Network ◽  
Great Promise ◽  
Expression Data ◽  
Network Information ◽  
Ppi Networks

Purpose: Burn is one of the most common injuries in clinical practice. The use of transcription factors (TFs) has been reported to reverse the epigenetic rewiring process and has great promise for skin regeneration. To better identify key TFs for skin reprogramming, we proposed a predictive system that conjoint analyzed gene expression data and regulatory network information. Methods: Firstly, the gene expression data in skin tissues were downloaded and the LIMMA package was used to identify differential-expressed genes (DEGs). Then three ways, including identification of TFs from the DEGs, enrichment analysis of TFs by a Fisher’s test, the direct and network-based influence degree analysis of TFs, were used to identify the key TFs related to skin regeneration. Finally, to obtain most comprehensive combination of TFs, the coverage extent of all the TFs were analyzed by Venn diagrams. Results: The top 30 TFs combinations with higher coverage were acquired. Especially, TFAP2A, ZEB1, and NFKB1 exerted greater regulatory influence on other DEGs in the local network and presented relatively higher degrees in the protein–protein interaction (PPI) networks. Conclusion: These TFs identification could give a deeper understanding of the molecular mechanism of cell trans-differentiation, and provide a reference for the skin regeneration and burn treatment.

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