scholarly journals Improving Gene Regulatory Network Inference by Incorporating Rates of Transcriptional Changes

2016 ◽  
Author(s):  
Jigar S. Desai ◽  
Ryan C. Sartor ◽  
Lovely Mae Lawas ◽  
SV Krishna Jagadish ◽  
Colleen J. Doherty
Keyword(s):  
Regulatory Networks ◽  
Network Inference ◽  
R Package ◽  
Rate Of Change ◽  
Model Systems ◽  
Data Sets ◽  
Expression Data ◽  
Model Species ◽  
Gene Regulatory Network Inference ◽  
Transcriptional Regulatory Networks

AbstractOrganisms respond to changes in their environment through transcriptional regulatory networks (TRNs). The regulatory hierarchy of these networks can be inferred from expression data. Computational approaches to identify TRNs can be applied in any species where quality RNA can be acquired, However, ChIP-Seq and similar validation methods are challenging to employ in non-model species. Improving the accuracy of computational inference methods can significantly reduce the cost and time of subsequent validation experiments. We have developed ExRANGES, an approach that improves the ability to computationally infer TRN from time series expression data. ExRANGES utilizes both the rate of change in expression and the absolute expression level to identify TRN connections. We evaluated ExRANGES in five data sets from different model systems. ExRANGES improved the identification of experimentally validated transcription factor targets for all species tested, even in unevenly spaced and sparse data sets. This improved ability to predict known regulator-target relationships enhances the utility of network inference approaches in non-model species where experimental validation is challenging. We integrated ExRANGES with two different network construction approaches and it has been implemented as an R package available here: http://github.com/DohertyLab/ExRANGES. To install the package type: devtools::install_github(“DohertyLab/ExRANGES”)

PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data

2020 ◽  
Author(s):  
Gourab Ghosh Roy ◽  
Nicholas Geard ◽  
Karin Verspoor ◽  
Shan He
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
State Of The Art ◽  
Supplementary Information ◽  
Expression Data ◽  
Gene Regulatory Network Inference ◽  
Regulatory Interactions ◽  
Inference Algorithms ◽  
Gene Regulatory

Abstract Motivation Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles. Results To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets. Availability and implementation Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ­­Ensemble-based network aggregation improves the accuracy of gene network reconstruction

2013 ◽  
Author(s):  
Jeffrey D. Allen ◽  
Yang Xie ◽  
Guanghua Xiao
Keyword(s):  
Mrna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
R Package ◽  
Network Reconstruction ◽  
Specific Gene ◽  
Expression Data ◽  
Mrna Expression Data ◽  
Network Aggregation ◽  
Context Specific

Reverse engineering approaches to construct context-specific gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings. However, the reliability and reproducibility of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improve the accuracy of the network topology constructed from mRNA expression data. To evaluate the performance of different approaches, we created dozens of simulated networks and also tested our methods on three Escherichia coli datasets. We demonstrate three novel applications from this development. First, bootstrapping can be done on the available samples, turning any network reconstruction approach into an ensemble method. Second, this aggregation approach can be used to combine GRNs from different network inference methods, creating a novel network reconstruction approach that consistently outperforms any constituent method. Third, the approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We are releasing an implementation of these techniques as an R package “ENA” which is able to run network inference in parallel across multiple servers. We made all of the code and data used in our simulations and analysis available online at https://github.com/QBRC/ENA-Research to ensure the reproducibility of our results.

scholarly journals ­­Ensemble-based network aggregation improves the accuracy of gene network reconstruction

2013 ◽  
Author(s):  
Jeffrey D. Allen ◽  
Yang Xie ◽  
Guanghua Xiao
Keyword(s):  
Mrna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
R Package ◽  
Network Reconstruction ◽  
Specific Gene ◽  
Expression Data ◽  
Mrna Expression Data ◽  
Network Aggregation ◽  
Context Specific

Reverse engineering approaches to construct context-specific gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings. However, the reliability and reproducibility of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improve the accuracy of the network topology constructed from mRNA expression data. To evaluate the performance of different approaches, we created dozens of simulated networks and also tested our methods on three Escherichia coli datasets. We demonstrate three novel applications from this development. First, bootstrapping can be done on the available samples, turning any network reconstruction approach into an ensemble method. Second, this aggregation approach can be used to combine GRNs from different network inference methods, creating a novel network reconstruction approach that consistently outperforms any constituent method. Third, the approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We are releasing an implementation of these techniques as an R package “ENA” which is able to run network inference in parallel across multiple servers. We made all of the code and data used in our simulations and analysis available online at https://github.com/QBRC/ENA-Research to ensure the reproducibility of our results.

Overview of Gene Regulatory Network Inference Based on Differential Equation Models

2020 ◽  
Vol 21 (11) ◽  
pp. 1054-1059
Author(s):  
Bin Yang ◽  
Yuehui Chen
Keyword(s):  
Differential Equation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
New Drugs ◽  
Gene Regulatory Network Inference ◽  
Ode Models ◽  
Differential Equation Models ◽  
Linear Ode ◽  
Gene Regulatory

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382090911
Author(s):  
Qi-en He ◽  
Yi-fan Tong ◽  
Zhou Ye ◽  
Li-xia Gao ◽  
Yi-zhi Zhang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Treatment Options ◽  
Genomic Data ◽  
Full Potential ◽  
Gene Regulatory Network Inference ◽  
Signature Genes

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

scholarly journals Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nisar Wani ◽  
Debmalya Barh ◽  
Khalid Raza
Keyword(s):  
Network Analysis ◽  
Mrna Expression ◽  
Mirna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
Expression Profiles ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Expression Data ◽  
Cancer Data

Abstract Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

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