ArticleScientific reports2017
Enhancing gene regulatory network inference through data integration with markov random fields.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 40 citations in OpenAlex.
- Using single cell atlas data to reconstruct regulatory networks.Nucleic acids research · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Functional Identification ofBiomolecules · 2022Article
- System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks.Frontiers in bioengineering and biotechnology · 2022Article
- Three topological features of regulatory networks control life-essential and specialized subsystems.Scientific reports · 2021Article
- Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance.Frontiers in genetics · 2021Article
- Use of Dual RNA-seq for Systems Biology Analysis ofFrontiers in microbiology · 2020Article
- BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus.BMC bioinformatics · 2019Article
- Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.BMC systems biology · 2019Article
- Reprogramming of regulatory network using expression uncovers sex-specific gene regulation in Drosophila.Nature communications · 2018Article
- Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize.BMC plant biology · 2018Article
- Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets.Frontiers in plant science · 2018Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.