ArticleFrontiers in bioinformatics2022
Integration of multi-omics technologies for crop improvement: Status and prospects.
Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Exploring agro-ecological significance, knowledge gaps, and research priorities in arbuscular mycorrhizal fungi.Frontiers in microbiology · 2024Pooled it
- Application of Omics Technologies for Cowpea Improvement.Plant-environment interactions (Hoboken, N.J.) · 2026Review
- AI-Guided DNA-Free and Genotype-Independent Genome Editing for Soybean Improvement.Plants (Basel, Switzerland) · 2026Review
- Halophilic bacteria and archaea in salinity-resilient agriculture: mechanisms and multi-omics perspectives.Archives of microbiology · 2026Review
- From Shared Mechanisms to Precision Breeding: Engineering Cold and Drought Cross-Tolerance in Crops.International journal of molecular sciences · 2026Review
- Bridging the Divide: Integrating Cottonseed Oil Content with Agronomic Trait Improvement in Upland Cotton (Plants (Basel, Switzerland) · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
- Impact of exercise intervention on immune function during aging.Frontiers in immunology · 2026Review
- Graph pan-genome advances genetic discoveries and the improvement of eggplant.Horticulture research · 2026Article
- The Ionome-Hormone-Flavonoid Network Shapes Genotype-Dependent Yield Adaptation in Sugarcane.Plants (Basel, Switzerland) · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Unlocking Plant Resilience: Metabolomic Insights into Abiotic Stress Tolerance in Crops.Metabolites · 2025Review
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Can the molecular and transgenic breeding of crops be an alternative and sustainable technology to meet food demand?Functional & integrative genomics · 2025Review
- Integration of multi-omics data and deep phenotyping provides insights into responses to single and combined abiotic stress in potato.Plant physiology · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- From data to decisions: a paradigm shift in fruit agriculture through the integration of multi-omics, modern phenotyping, and cutting-edge bioinformatic tools.Frontiers in plant science · 2025Review
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Mapping and Omics Integration: Towards Precise Rice Disease Resistance Breeding.Plants (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.
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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.