ReviewPlants (Basel, Switzerland)2019
Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding.
Review in Plants (Basel, Switzerland), 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.
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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
28 citing papers in PubMed, 84 citations in OpenAlex.
- Manifold-based learning for high-throughput single-peanut phenotyping.NPJ systems biology and applications · 2026Article
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- The Genomics Revolution in Nonmodel Species: Predictions vs. Reality for Salmonids.Molecular ecology · 2025Review
- Genomics-assisted breeding for designing salinity-smart future crops.Plant biotechnology journal · 2025Review
- Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity.Plant cell reports · 2024Review
- Multi-omics assists genomic prediction of maize yield with machine learning approaches.Molecular breeding : new strategies in plant improvement · 2024Article
- How Plants Tolerate Salt Stress.Current issues in molecular biology · 2023Review
- The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement.Current genomics · 2023Review
- Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.Frontiers in plant science · 2023Article
- Tracing Back the History of Pepper (Plants (Basel, Switzerland) · 2022Article
- Prospects of genetics and breeding for low-phosphate tolerance: an integrated approach from soil to cell.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Review
- Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.International journal of molecular sciences · 2022Review
- Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow.Plants (Basel, Switzerland) · 2022Article
- Whole-genome resequencing reveals genomic footprints of Italian sweet and hot pepper heirlooms giving insight into genes underlying key agronomic and qualitative traits.BMC genomic data · 2022Article
- Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance.International journal of molecular sciences · 2022Review
- Smart breeding approaches in post-genomics era for developing climate-resilient food crops.Frontiers in plant science · 2022Review
- Tomato genomic prediction for good performance under high-temperature and identification of loci involved in thermotolerance response.Horticulture research · 2021Article
- Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions.Journal of plant research · 2021Article
- Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Advances in Cereal Crop Genomics for Resilience under Climate Change.Life (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.
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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.