ArticleThe Plant cell2014
Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis.
Article in The Plant cell, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 3 of them syntheses that pooled it.
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Who cites it
76 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.Nature communications · 2025Pooled it
- Differential networking meta-analysis of gastric cancer across Asian and American racial groups.BMC systems biology · 2018Pooled it
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Integrative machine learning and gene regulatory network analysis identifies novel genes for leafy head formation in Brassica rapa and B. oleracea.Molecular horticulture · 2026Article
- Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selection.Genetics, selection, evolution : GSE · 2026Article
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.Nature communications · 2026Article
- Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support.Plant phenomics (Washington, D.C.) · 2026Review
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.Scientific reports · 2025Article
- Genome-wide expression atlas of tomato flower buds revealed theHorticulture research · 2025Article
- Interspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development.Journal of experimental botany · 2025Article
- Integrative machine learning and RT-qPCR analysis identify key stress-responsive genes in Thermus thermophilus HB8.Genetica · 2025Article
- Core Perturbomes ofPathogens (Basel, Switzerland) · 2025Article
- Unveiling Salt Tolerance Mechanisms in Plants: Integrating the KANMB Machine Learning Model With Metabolomic and Transcriptomic Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine learning-augmented m6A-Seq analysis without a reference genome.Briefings in bioinformatics · 2025Article
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Enhancing genomic prediction inFrontiers in bioinformatics · 2025Article
- Confronting the data deluge: How artificial intelligence can be used in the study of plant stress.Computational and structural biotechnology journal · 2024Review
16 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.
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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.