ArticleProceedings of the National Academy of Sciences of the United States of America2019
Identification of the expressome by machine learning on omics data.
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. Cited by 28 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
28 citing papers in PubMed, 80 citations in OpenAlex.
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.Interdisciplinary sciences, computational life sciences · 2024Article
- The Progression in Developing Genomic Resources for Crop Improvement.Life (Basel, Switzerland) · 2023Review
- Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants.Plant communications · 2023Review
- Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences.Nature biomedical engineering · 2023Article
- Epigenetic Changes Occurring in Plant Inbreeding.International journal of molecular sciences · 2023Review
- Integrating speed breeding with artificial intelligence for developing climate-smart crops.Molecular biology reports · 2022Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Epigenome guided crop improvement: current progress and future opportunities.Emerging topics in life sciences · 2022Review
- Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information.The Plant cell · 2022Article
- Epigenetic stress memory: A new approach to study cold and heat stress responses in plants.Frontiers in plant science · 2022Review
- Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.Frontiers in artificial intelligence · 2022Article
- Leveraging histone modifications to improve genome annotations.G3 (Bethesda, Md.) · 2021Article
- Stability of DNA methylation and chromatin accessibility in structurally diverse maize genomes.G3 (Bethesda, Md.) · 2021Article
- Natural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach.BMC plant biology · 2021Article
- Systems biology for crop improvement.The plant genome · 2021Review
- Comparative Transcriptome Analysis Reveals Genetic Mechanisms of Sugarcane Aphid Resistance in Grain Sorghum.International journal of molecular sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate annotation of plant genomes remains complex due to the presence of many pseudogenes arising from whole-genome duplication-generated redundancy or the capture and movement of gene fragments by transposable elements. Machine learning on genome-wide epigenetic marks, informed by transcriptomic and proteomic training data, could be used to improve annotations through classification of all putative protein-coding genes as either constitutively silent or able to be expressed. Expressed genes were subclassified as able to express both mRNAs and proteins or only RNAs, and CG gene body methylation was associated only with the former subclass. More than 60,000 protein-coding genes have been annotated in the reference genome of maize inbred B73. About two-thirds of these genes are transcribed and are designated the filtered gene set (FGS). Classification of genes by our trained random forest algorithm was accurate and relied only on histone modifications or DNA methylation patterns within the gene body; promoter methylation was unimportant. Other inbred lines are known to transcribe significantly different sets of genes, indicating that the FGS is specific to B73. We accurately classified the sets of transcribed genes in additional inbred lines, arising from inbred-specific DNA methylation patterns. This approach highlights the potential of using chromatin information to improve annotations of functional genes.
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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.