ReviewApplications in plant sciences2020
Machine learning: A powerful tool for gene function prediction in plants.
Review in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Drought-responsive genes in tomato: meta-analysis of gene expression using machine learning.Scientific reports · 2023Pooled it
- BioprospectingFrontiers in fungal biology · 2021Pooled it
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Genetic basis of alkaloid divergence in the Solanaceae.Plant molecular biology · 2026Article
- Reimagining plant science training in the era of generative artificial intelligence: a global perspective.The Plant cell · 2026Review
- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Post-transcriptional regulation of light-stress responses and predictive modeling in vegetableFrontiers in plant science · 2026Review
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- Perspectives on morphology, physiology, genetic polymorphism and machine learning in cucumber grafting under zinc toxicity.BMC plant biology · 2025Article
- A conceptual framework for human-AI collaborative genome annotation.Briefings in bioinformatics · 2025Review
- ChromoCheck: Predicting Postnatal Chromosomal Trisomy Cases Using a Support Vector Machine Learning Model.Genes · 2025Article
- Arabidopsis as a model for translational research.The Plant cell · 2025Review
- MicroRNAs in Plant Genetic Regulation of Drought Tolerance and Their Function in Enhancing Stress Adaptation.Plants (Basel, Switzerland) · 2025Review
- Comparative genomic prediction of resistance to Fusarium wilt (Fusarium oxysporum f. sp. niveum race 2) in watermelon: parametric and nonparametric approaches.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Extraction, Annotation, and Purification of Resin Glycosides from the Morning Glory Family (Convolvulaceae).Methods in molecular biology (Clifton, N.J.) · 2025Article
- FuncFetch: an LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts.Bioinformatics (Oxford, England) · 2024Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.Genetics · 2024Article
- Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review.Plants (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.
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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.