ArticleFrontiers in plant science2016
A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis.
Article in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants.Methods in molecular biology (Clifton, N.J.) · 2023Article
- RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors.International journal of molecular sciences · 2022Article
- Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.International journal of molecular sciences · 2022Review
- Advances in Cereal Crop Genomics for Resilience under Climate Change.Life (Basel, Switzerland) · 2021Review
- Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks.Frontiers in plant science · 2021Review
- Determinants of correlated expression of transcription factors and their target genes.Nucleic acids research · 2020Article
- Prediction of condition-specific regulatory genes using machine learning.Nucleic acids research · 2020Article
- AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana.Nucleic acids research · 2020Article
- Article
- On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties.Frontiers in plant science · 2018Article
- Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets.Frontiers in plant science · 2018Review
- Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds.Frontiers in plant science · 2017Article
- Multilevel Regulation of Abiotic Stress Responses in Plants.Frontiers in plant science · 2017Review
- Machine learning in postgenomic biology and personalized medicine.Wiley interdisciplinary reviews. Data mining and knowledge discoveryArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing
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Registered trials
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.