ArticleProceedings of the National Academy of Sciences of the United States of America2019
Robust predictions of specialized metabolism genes through machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 51 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.
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.
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Who cites it
51 citing papers in PubMed.
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.Genome research · 2026Article
- Advances in Functional Genomics and Biotechnology for Enhancing Therapeutic Potential of Medicinal Plants.International journal of molecular sciences · 2026Review
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- An Introduction to Network Analysis in Plant Biology.Plant direct · 2025Article
- CoExpPhylo - a novel pipeline for biosynthesis gene discovery.BMC genomics · 2025Article
- Interspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development.Journal of experimental botany · 2025Article
- Machine learning reveals genes impacting oxidative stress resistance across yeasts.Nature communications · 2025Article
- Predicting precursors of plant specialized metabolites using DeepMol automated machine learning.Journal of integrative bioinformatics · 2025Article
- 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
- Linking plant genes to arthropod community dynamics: current progress and future challenges.Plant & cell physiology · 2025Review
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- Understanding metabolic diversification in plants: branchpoints in the evolution of specialized metabolism.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- A developmental gradient reveals biosynthetic pathways to eukaryotic toxins in monocot geophytes.Cell · 2024Article
- Enhancing Withanolide Production in thePlants (Basel, Switzerland) · 2024Review
- Super-pigment: Deciphering shikonin biosynthesis to fight cancer, inflammation, and much more.Plant physiology · 2024Article
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- Machine learning enables identification of an alternative yeast galactose utilization pathway.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.BMC genomics · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using
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.