A comprehensive overview and critical evaluation of gene regulatory network inference technologies

Zhao, Mengyuan; He, Wenying; Tang, Jijun; Zou, Quan; Guo, Fei

doi:10.1093/bib/bbab009

Cited by 49 publications

(29 citation statements)

References 72 publications

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“…DPM (Ghanbari et al, 2019), sdcorGCN (Pardo-Diaz et al, 2021), PIDC (Zhao et al, 2016;Chan et al, 2017). These approaches (reviewed in (Fiers et al, 2018;Zhao et al, 2021;Emmert-Streib et al, 2012)) are based on the assumption that a change in TF This article is protected by copyright. All rights reserved.…”

Section: Tfs As Part Of Gene Regulatory Networkmentioning

confidence: 99%

“…Another set of methods are based on coexpression of TFs and genes (e.g., WGCNA [108]), with some variations that use energy-based or information-based measures instead of correlation (e.g., DPM [109], sdcorGCN [110], PIDC [111,112]. These approaches (reviewed in [113][114][115]) are based on the assumption that a change in TF expression level will result in a transcriptional change of its regulon. Despite the significant progress and numerous practical applications of co-expression to GRN inference their direct interpretation in terms of gene regulation is limited due to missing directionality.…”

Section: Tfs As Part Of Gene Regulatory Network (Grn)mentioning

confidence: 99%

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Transcription factors: Bridge between cell signaling and gene regulation

et al. 2021

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Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional programme. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post-translational modifications of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multiomics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.

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Section: Tfs As Part Of Gene Regulatory Networkmentioning

confidence: 99%

Section: Tfs As Part Of Gene Regulatory Network (Grn)mentioning

confidence: 99%

Transcription factors: Bridge between cell signaling and gene regulation

et al. 2021

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“…Thus, a large number of regulations should be removed from the derived network. We next use the dynamic model ( 9) with function (10) to remove regulations that has less impact on the system dynamics. With such a large number of regulations in the network, the removal of one or two regulations has limited effects on the system dynamics.…”

Section: Inference Network With Less Regulationsmentioning

confidence: 99%

“…The inference methods for constructing regulatory networks can be mainly classified into three major types, namely the correlation-based methods, dynamic model methods and machine learning methods [9][10][11]. The correlation-based methods use one or more statistical qualities to measure the relationship between pairs of variables in a network.…”

Section: Introductionmentioning

confidence: 99%

Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data

et al. 2021

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Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.

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“…Multiple computational methods can be used to learn GRNs from observational data, including correlation analysis [ 20 , 29 , 34 ], Boolean networks [ 31 , 36 ], Bayesian networks [ 5 , 12 , 59 ], differential equation models [ 60 , 61 ] and machine learning approaches [ 19 ]. A recent benchmarking study [ 63 ] revealed no clear winner among different methods for GRN reconstruction, with different methods demonstrating advantages in different settings.…”

Section: Introductionmentioning

confidence: 99%

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

Suter

Kuipers

Beerenwinkel

2022

Briefings in Bioinformatics

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Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their GRNs. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.

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A comprehensive overview and critical evaluation of gene regulatory network inference technologies

Cited by 49 publications

References 72 publications

Transcription factors: Bridge between cell signaling and gene regulation

Transcription factors: Bridge between cell signaling and gene regulation

Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

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