ArticleNucleic acids research2020
Prediction of condition-specific regulatory genes using machine learning.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Key Differentially Expressed Genes inInternational journal of molecular sciences · 2025Pooled it
- Design and Testing of Root-Specific Synthetic Promoters by Machine Learning.International journal of molecular sciences · 2026Article
- Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Article
- SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques.Frontiers in plant science · 2025Article
- Loss of cold tolerance is conferred by absence of the WRKY34 promoter fragment during tomato evolution.Nature communications · 2024Article
- Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress.BMC plant biology · 2024Article
- FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi.PLoS computational biology · 2023Article
- Functional investigation of five R2R3-MYB transcription factors associated with wood development in Eucalyptus using DAP-seq-ML.Plant molecular biology · 2023Article
- Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants.Plant communications · 2023Review
- Biocuration of a Transcription Factors Network Involved in Submergence Tolerance during Seed Germination and Coleoptile Elongation in Rice (Plants (Basel, Switzerland) · 2023Article
- Using single cell atlas data to reconstruct regulatory networks.Nucleic acids research · 2023Article
- Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Characterization of Gene Regulatory Networks in Plants Using New Methods and Data Types.Methods in molecular biology (Clifton, N.J.) · 2023Article
- CANTATA-prediction of missing links in Boolean networks using genetic programming.Bioinformatics (Oxford, England) · 2022Article
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- PGD: a machine learning-based photosynthetic-related gene detection approach.BMC bioinformatics · 2022Article
- Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods.The New phytologist · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5-25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.
Indexed as
Identifiers
What OpenQuestion holds
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.