ArticleThe Plant cell2011
Functional network construction in Arabidopsis using rule-based machine learning on large-scale data sets.
Article in The Plant cell, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed.
- CYSTEINE-RICH RLK2 regulates development via callose synthase-dependent symplastic transport in Arabidopsis.Plant physiology · 2026Article
- CRK2 modulates flowering in Arabidopsis together with GLYCINE-RICH RNA-BINDING PROTEIN 7.The New phytologist · 2026Article
- A retrotransposon insertion upstream of Arabidopsis thaliana CRK8 receptor-like kinase modulates a trade-off between pathogen defense and salt tolerance.The Plant journal : for cell and molecular biology · 2025Article
- Dissecting the temporal genetic networks programming soybean embryo development from embryonic morphogenesis to post-germination.Plant cell reports · 2024Article
- A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae.Plants (Basel, Switzerland) · 2022Article
- Article
- Addressing noise in co-expression network construction.Briefings in bioinformatics · 2022Review
- Integrated Analysis of mRNA and MicroRNA Co-expressed Network for the Differentiation of Bovine Skeletal Muscle Cells After Polyphenol Resveratrol Treatment.Frontiers in veterinary science · 2021Article
- Co-expression Gene Network Analysis and Functional Module Identification in Bamboo Growth and Development.Frontiers in genetics · 2018Article
- Differentially expressed genes during the imbibition of dormant and after-ripened seeds - a reverse genetics approach.BMC plant biology · 2017Article
- Seed biology - from lab to field.Journal of experimental botany · 2017Article
- Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine.Frontiers in pharmacology · 2017Review
- Co-expression network analyses identify functional modules associated with development and stress response in Gossypium arboreum.Scientific reports · 2016Article
- Functional networks inference from rule-based machine learning models.BioData mining · 2016Article
- Phytohormones Signaling Pathways and ROS Involvement in Seed Germination.Frontiers in plant science · 2016Review
- A survey of computational tools for downstream analysis of proteomic and other omic datasets.Human genomics · 2015Review
- Large-Scale Phenomics Identifies Primary and Fine-Tuning Roles for CRKs in Responses Related to Oxidative Stress.PLoS genetics · 2015Article
- Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis.Plant physiology · 2015Article
- Towards a better monitoring of seed ageing under ex situ seed conservation.Conservation physiology · 2015Article
- A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The meta-analysis of large-scale postgenomics data sets within public databases promises to provide important novel biological knowledge. Statistical approaches including correlation analyses in coexpression studies of gene expression have emerged as tools to elucidate gene function using these data sets. Here, we present a powerful and novel alternative methodology to computationally identify functional relationships between genes from microarray data sets using rule-based machine learning. This approach, termed "coprediction," is based on the collective ability of groups of genes co-occurring within rules to accurately predict the developmental outcome of a biological system. We demonstrate the utility of coprediction as a powerful analytical tool using publicly available microarray data generated exclusively from Arabidopsis thaliana seeds to compute a functional gene interaction network, termed Seed Co-Prediction Network (SCoPNet). SCoPNet predicts functional associations between genes acting in the same developmental and signal transduction pathways irrespective of the similarity in their respective gene expression patterns. Using SCoPNet, we identified four novel regulators of seed germination (ALTERED SEED GERMINATION5, 6, 7, and 8), and predicted interactions at the level of transcript abundance between these novel and previously described factors influencing Arabidopsis seed germination. An online Web tool to query SCoPNet has been developed as a community resource to dissect seed biology and is available at http://www.vseed.nottingham.ac.uk/.
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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.