ReviewApplied microbiology and biotechnology2022
Machine learning: its challenges and opportunities in plant system biology.
Review in Applied microbiology and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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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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Who cites it
36 citing papers in PubMed, 76 citations in OpenAlex.
- Structural Variation and Its Roles in Plant Genomes.Plants (Basel, Switzerland) · 2026Review
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Ethylene as the Molecular Coordinator of the Plant Growth-Defense Trade-Off Under Biotic and Abiotic Stresses.International journal of molecular sciences · 2026Review
- LILRB3 inhibition reverses immunosuppression in glioma: a nanoparticle-based therapeutic strategy.Journal of nanobiotechnology · 2026Article
- Plant health in the era of global changes, holobiont biology, and microbiome-based solutions.Horticulture research · 2026Article
- A metabolic-inflammatory phenotype of pelvic floor dysfunction: A machine learning-based cross-sectional study in a nationally representative US population.The Journal of international medical research · 2026Article
- Article
- Antioxidant Defense Systems in Plants: Mechanisms, Regulation, and Biotechnological Strategies for Enhanced Oxidative Stress Tolerance.Life (Basel, Switzerland) · 2025Review
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- 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
- Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery.Plant physiology · 2025Article
- The Effect of Naturally Acquired Immunity on Mortality Predictors: A Focus on Individuals with New Coronavirus.Biomedicines · 2025Article
- Machine Learning-Aided Optimization of In Vitro Tetraploid Induction in Cannabis.International journal of molecular sciences · 2025Article
- The role of statistics in advancing nitric oxide research in plant biology: from data analysis to mechanistic insights.Frontiers in plant science · 2025Review
- Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing.PLoS computational biology · 2024Article
- A Reinforcement Learning approach to study climbing plant behaviour.Scientific reports · 2024Article
- Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review.Plants (Basel, Switzerland) · 2024Review
- Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (HPlant molecular biology · 2024Article
- Genomic data integration tutorial, a plant case study.BMC genomics · 2024Article
- Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.PloS one · 2024Article
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.
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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.