{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T22:11:23Z","timestamp":1787263883811,"version":"build-2736575974"},"reference-count":124,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Plant phytohormone networks control growth, dormancy, and stress responses throughout plants\u2019 long lifespans and heterogeneous tissues. While multi-omics approaches have advanced the understanding of these regulatory systems, their application remains constrained by low spatial resolution, static sampling strategies, and limited capacity to capture nonlinear, context-dependent interactions. These limitations are especially evident in forest tree species, where hormone gradients change dynamically across developmental stages, seasons, and environmental conditions. The emergence of Artificial Intelligence (AI), including its subsets deep learning (DL) and machine learning (ML), provides valuable solutions to overcome these barriers. These solutions involve integrating high-dimensional data, reconstructing spatiotemporal hormone architectures, and developing predictive network models that regulate stress resilience and growth. This review highlights recent research on integrative omics in forest hormone biology and discusses how AI technologies overcome the barriers in traditional multi-omics approaches. Additionally, it outlines future directions for developing translational tools to support sustainable forestry production and management.<\/jats:p>","DOI":"10.3390\/plants15142185","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:55:59Z","timestamp":1784249759000},"page":"2185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology"],"prefix":"10.3390","volume":"15","author":[{"given":"Yao","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Tree Genetics and Breeding, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tree Genetics and Breeding, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liming","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tree Genetics and Breeding, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhui","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tree Genetics and Breeding, College of Forestry, Nanjing Forestry University, Nanjing 213007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2978-4171","authenticated-orcid":false,"given":"Delight","family":"Hwarari","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tree Genetics and Breeding, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China"},{"name":"State Key Laboratory of Tree Genetics and Breeding, College of Forestry, Nanjing Forestry University, Nanjing 213007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ding, W., Wang, C., Mei, M., Li, X., Zhang, Y., Lin, H., Li, Y., Ma, Z., Han, J., and Song, X. (2024). Phytohormones involved in vascular cambium activity in woods: Current progress and future challenges. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1508242"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Anand, S., and Chaudhary, N. (2024). Integrating multi-omics approaches for crop resilience under changing climatic conditions. Microbial Omics in Environment and Health, Springer.","DOI":"10.1007\/978-981-97-1769-9_11"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Samanta, S., and Roychoudhury, A. (2024). Omics and Bioinformatics Approaches for Abiotic Stress Tolerance in Plants. Bioinformatics for Plant Research and Crop Breeding, Springer Nature.","DOI":"10.1002\/9781394209965.ch7"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.hpj.2023.09.005","article-title":"Genomics, Phenomics, and Machine Learning in Shaping the Future of Plant Research: Advancements and Challenges","volume":"11","author":"Mansoor","year":"2024","journal-title":"Hortic. Plant J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s11032-024-01454-z","article-title":"Multi-omics assists genomic prediction of maize yield with machine learning approaches","volume":"44","author":"Wu","year":"2024","journal-title":"Mol. Breed."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, T., Zuo, Y., Manda, T., Hwarari, D., and Yang, L. (2025). Harnessing Artificial Intelligence, Machine Learning and Deep Learning for Sustainable Forestry Management and Conservation: Transformative Potential and Future Perspectives. Plants, 14.","DOI":"10.3390\/plants14070998"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101668","DOI":"10.1016\/j.xplc.2025.101668","article-title":"Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design","volume":"7","author":"Kundu","year":"2025","journal-title":"Plant Commun."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Manoj, M., Anand, P.P., Devi, T.N., Bharathi, V., Manikandan, R., Prabhu, J.P.A., Robert, A.A., and Anand, A.V. (2026). Deep generative models for studying and integrating multiple plant omics. AI Technologies for Crop Breeding, Elsevier.","DOI":"10.1016\/B978-0-443-33633-1.00009-5"},{"key":"ref_9","first-page":"22","article-title":"A Systems Biology Perspective on Plant Hormonal Signaling Networks","volume":"1","author":"Romano","year":"2025","journal-title":"Plant Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/S1672-0229(08)60040-6","article-title":"How Do Variable Substitution Rates Influence Ka and Ks Calculations?","volume":"7","author":"Wang","year":"2009","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sabir, I.A., Hu, X., Khan, I., and Qin, Y. (2025). Regulatory mechanisms of bud dormancy: Environmental, hormonal, and genetic perspectives. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26062517"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, L., and Liu, Y. (2024). Identification, design, and application of noncoding cis-regulatory elements. Biomolecules, 14.","DOI":"10.3390\/biom14080945"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e733","DOI":"10.1002\/cpz1.733","article-title":"ntLink: A toolkit for de novo genome assembly scaffolding and mapping using long reads","volume":"3","author":"Coombe","year":"2023","journal-title":"Curr. Protoc."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Satrio, R.D., Fendiyanto, M.H., and Miftahudin, M. (2024). Tools and techniques used at global scale through genomics, transcriptomics, proteomics, and metabolomics to investigate plant stress responses at the molecular level. Molecular Dynamics of Plant Stress and Its Management, Springer.","DOI":"10.1007\/978-981-97-1699-9_25"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1038\/s41576-025-00828-z","article-title":"Transcriptomics in the era of long-read sequencing","volume":"26","author":"Liu","year":"2025","journal-title":"Nat. Rev. Genet."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tong, L., Liu, H., Li, B., and Zhang, R. (2025). Integrated metabolome and transcriptome analysis of maize roots response to different degrees of drought stress. BMC Plant Biol., 25.","DOI":"10.1186\/s12870-025-06505-x"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Marku, M., and Pancaldi, V. (2023). From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput. Biol., 19.","DOI":"10.1371\/journal.pcbi.1011254"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cheng, C., Chen, W., Jin, H., and Chen, X. (2023). A review of single-cell RNA-seq annotation, integration, and cell\u2013cell communication. Cells, 12.","DOI":"10.3390\/cells12151970"},{"key":"ref_19","unstructured":"Liu, Q. (2023). An Analysis of Reproductive Phase Transition in Maize. [Ph.D. Thesis, Iowa State University]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1093\/jxb\/erac324","article-title":"Advances in mass spectrometry-based phosphoproteomics for elucidating abscisic acid signaling and plant responses to abiotic stress","volume":"73","author":"Chong","year":"2022","journal-title":"J. Exp. Bot."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Soma, F., Takahashi, F., Yamaguchi-Shinozaki, K., and Shinozaki, K. (2021). Cellular Phosphorylation Signaling and Gene Expression in Drought Stress Responses: ABA-Dependent and ABA-Independent Regulatory Systems. Plants, 10.","DOI":"10.3390\/plants10040756"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s12374-026-09505-y","article-title":"A New Paradigm for the Stomatal Closures: ABA-Dependent Stomatal Closures and ABA-Independent Stomatal Closures","volume":"69","author":"Lee","year":"2026","journal-title":"J. Plant Biol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"uhad238","DOI":"10.1093\/hr\/uhad238","article-title":"Integrated proteomic analysis reveals interactions between phosphorylation and ubiquitination in rose response to Botrytis infection","volume":"11","author":"Li","year":"2024","journal-title":"Hortic. Res."},{"key":"ref_24","unstructured":"Desiderio, D.M., and Loo, J.A. (2023). Monitoring Forest Damage with Mass Spectrometry-Based Metabolomics Methods, John Wiley & Sons."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yadav, A., Bohra, N., and Singh, H. (2024). Forest ecosystem adaptation to climate change: Morphological and physiological responses. Forests and Climate Change: Biological Perspectives on Impact, Adaptation, and Mitigation Strategies, Springer.","DOI":"10.1007\/978-981-97-3905-9_14"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, T., Tabassum, B., Hashim, M., and Khan, M. (2026). Emerging trends in metabolomics research. Synergistic Plant Metabolomics and Plant Growth-Promoting Microorganisms in Addressing Abiotic Stress, Academic Press.","DOI":"10.1016\/B978-0-443-33026-1.00021-9"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ribbenstedt, A., Ziarrusta, H., and Benskin, J.P. (2018). Development, characterization and comparisons of targeted and non-targeted metabolomics methods. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207082"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, J., Sun, X., Yao, Y., Liu, Y., Yang, D., Yang, H., Yu, J., Zheng, D., and Wu, Y. (2025). Integration of Transcriptome, miRNA-Omics, and Hormone Metabolism Analysis Reveals the Regulatory Network of Camellia drupifera Fruit Maturation. Plants, 14.","DOI":"10.3390\/plants14213282"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5267","DOI":"10.1093\/jxb\/erae054","article-title":"Epigenetics and plant hormone dynamics: A functional and methodological perspective","volume":"75","author":"Rudolf","year":"2024","journal-title":"J. Exp. Bot."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1042\/EBC20190027","article-title":"Latest techniques to study DNA methylation","volume":"63","author":"Gouil","year":"2019","journal-title":"Essays Biochem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e99010019","DOI":"10.14440\/jbm.2024.0017","article-title":"The development and application of cleavage under targets and tagmentation (CUT&Tag) technology","volume":"11","author":"Xiong","year":"2024","journal-title":"J. Biol. Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1186\/s13059-022-02768-x","article-title":"DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis","volume":"23","author":"Zhao","year":"2022","journal-title":"Genome Biol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108677","DOI":"10.1016\/j.plaphy.2024.108677","article-title":"Integrated physiological, transcriptomic and metabolomic analyses provide insights into phosphorus-mediated cadmium detoxification in Salix caprea roots","volume":"211","author":"Li","year":"2024","journal-title":"Plant Physiol. Biochem."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sui, D., Wang, B., El-Kassaby, Y.A., and Wang, L. (2024). Integration of Physiological, Transcriptomic, and Metabolomic Analyses Reveal Molecular Mechanisms of Salt Stress in Maclura tricuspidata. Plants, 13.","DOI":"10.3390\/plants13030397"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105373","DOI":"10.1016\/j.jprot.2024.105373","article-title":"Multi-omics analysis reveals the regulatory mechanism of branching development in Quercus fabri","volume":"313","author":"Xiong","year":"2025","journal-title":"J. Proteom."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, G., Zhang, G., Wu, Z., Lu, W., Lin, Y., Wang, C., Shang, X., Huang, A., and Luo, J. (2024). Comparative proteomic analysis provides insights into wood formation in immature xylem at different ages in Eucalyptus urophylla\u00d7 Eucalyptus grandis. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1431164"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"116284","DOI":"10.1016\/j.indcrop.2023.116284","article-title":"Transcriptome and proteome analyses reveal a critical role of the sucrose anabolic pathway in regulating Quercus fabri Hance axillary bud outgrowth","volume":"194","author":"Xiong","year":"2023","journal-title":"Ind. Crops Prod."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, G., Wu, Z., Luo, J., Wang, C., Shang, X., and Zhang, G. (2023). Genes expression profiles in vascular cambium of Eucalyptus urophylla\u00d7 Eucalyptus grandis at different ages. BMC Plant Biol., 23.","DOI":"10.1186\/s12870-023-04500-8"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s11103-024-01458-5","article-title":"Transcriptome analysis and functional validation reveal the novel role of LhCYCL in axillary bud development in hybrid Liriodendron","volume":"114","author":"Wen","year":"2024","journal-title":"Plant Mol. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1093\/plphys\/kiac272","article-title":"Drought induces epitranscriptome and proteome changes in stem-differentiating xylem of Populus trichocarpa","volume":"190","author":"Gao","year":"2022","journal-title":"Plant Physiol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, D., Janz, D., Zienkiewicz, K., Herrfurth, C., Feussner, I., Chen, S., and Polle, A. (2021). Wood Formation under Severe Drought Invokes Adjustment of the Hormonal and Transcriptional Landscape in Poplar. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22189899"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kan, Z., Xu, Y., Li, G., Wang, W., Wang, P., and Zhou, C. (2026). Multi-Omics Analysis Sheds Light on the Relative Roles of Hormones and Nutrients in Regulating Secondary Flowering in Prunus subhirtella \u2018Autumnalis\u2019. Plants, 15.","DOI":"10.3390\/plants15050812"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"109703","DOI":"10.1016\/j.plaphy.2025.109703","article-title":"Integrative analysis of transcriptome, proteome, and phosphoproteome reveals the complexity of early nitrogen responses in poplar roots","volume":"222","author":"Chang","year":"2025","journal-title":"Plant Physiol. Biochem."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ali, S., Tahir, S., Hassan, S.S., Lu, M., Wang, X., Quyen, L.T.Q., Zhang, W., and Chen, S. (2025). The role of phytohormones in mediating drought stress responses in Populus species. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26083884"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Damaris, R.N., and Yang, P. (2021). Protein phosphorylation response to abiotic stress in plants. Plant Phosphoproteomics: Methods and Protocols, Humana.","DOI":"10.1007\/978-1-0716-1625-3_2"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1021\/acs.jproteome.9b00600","article-title":"Phosphoproteomic and Metabolomic Analyses Reveal Sexually Differential Regulatory Mechanisms in Poplar to Nitrogen Deficiency","volume":"19","author":"Song","year":"2020","journal-title":"J. Proteome Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"tpaf124","DOI":"10.1093\/treephys\/tpaf124","article-title":"Phosphorus application enhances cadmium tolerance in Salix caprea via physiological and anatomical adjustments","volume":"45","author":"Li","year":"2025","journal-title":"Tree Physiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1093\/treephys\/tpac013","article-title":"Integrated DNA methylation, transcriptome and physiological analyses reveal new insights into superiority of poplars formed by interspecific grafting","volume":"42","author":"Han","year":"2022","journal-title":"Tree Physiol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhao, F., and Zhou, B. (2022). The characters of non-coding RNAs and their biological roles in plant development and abiotic stress response. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23084124"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"MSB178124","DOI":"10.15252\/msb.20178124","article-title":"Multi-Omics Factor Analysis\u2014A framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol. Syst. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1177932219899051","DOI":"10.1177\/1177932219899051","article-title":"Multi-omics data integration, interpretation, and its application","volume":"14","author":"Subramanian","year":"2020","journal-title":"Bioinform. Biol. Insights"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s13595-015-0488-3","article-title":"Forest tree genomics: 10 achievements from the past 10 years and future prospects","volume":"73","author":"Plomion","year":"2016","journal-title":"Ann. For. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, H. (2024). Endogenous and environmental signals in regulating vascular development and secondary growth. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1369241"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1177\/15578100251392371","article-title":"Integrative Multi-Omics and Artificial Intelligence: A New Paradigm for Systems Biology","volume":"29","author":"Kant","year":"2025","journal-title":"OMICS J. Integr. Biol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Verma, V., Ravindran, P., and Kumar, P.P. (2016). Plant hormone-mediated regulation of stress responses. BMC Plant Biol., 16.","DOI":"10.1186\/s12870-016-0771-y"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41570-022-00443-0","article-title":"Continuous monitoring of chemical signals in plants under stress","volume":"7","author":"Coatsworth","year":"2023","journal-title":"Nat. Rev. Chem."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yasrab, R., Zhang, J., Smyth, P., and Pound, M.P. (2021). Predicting plant growth from time-series data using deep learning. Remote Sens., 13.","DOI":"10.3390\/rs13030331"},{"key":"ref_58","first-page":"100012","article-title":"Integrating Multi-Omics Data for Plant Stress Response: Current Advances and Future Directions","volume":"3","author":"Kajrolkar","year":"2025","journal-title":"Prem. J. Plant Biol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/jxb\/ers287","article-title":"Xylem tissue specification, patterning, and differentiation mechanisms","volume":"64","author":"Schuetz","year":"2013","journal-title":"J. Exp. Bot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1007\/s00299-024-03383-9","article-title":"Single-cell transcriptomics: A new frontier in plant biotechnology research","volume":"43","author":"Singh","year":"2024","journal-title":"Plant Cell Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11738-025-03877-3","article-title":"Wound-induced dynamics of selected auxins and jasmonates suggest interhormonal crosstalk during the induction phase of adventitious root formation in Prunus subhirtella \u2018Autumnalis\u2019","volume":"48","author":"Kunc","year":"2026","journal-title":"Acta Physiol. Plant."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, M., Li, R., and Zhao, Q. (2023). Multi-omics techniques in genetic studies and breeding of forest plants. Forests, 14.","DOI":"10.3390\/f14061196"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Day, M.E., and Greenwood, M.S. (2011). Regulation of ontogeny in temperate conifers. Size-and Age-Related Changes in Tree Structure and Function, Springer.","DOI":"10.1007\/978-94-007-1242-3_4"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1111\/tpj.15124","article-title":"Multiple levels of crosstalk in hormone networks regulating plant defense","volume":"105","author":"Aerts","year":"2021","journal-title":"Plant J."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1089\/cmb.2021.0598","article-title":"SDGCCA: Supervised deep generalized canonical correlation analysis for multi-omics integration","volume":"29","author":"Moon","year":"2022","journal-title":"J. Comput. Biol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chaturvedi, P., Pierides, I., Zhang, S., Schwarzerova, J., Ghatak, A., and Weckwerth, W. (2024). Multiomics for crop improvement. Frontier Technologies for Crop Improvement, Springer.","DOI":"10.1007\/978-981-99-4673-0_6"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ejigu, G.F., and Jung, J. (2020). Review on the computational genome annotation of sequences obtained by next-generation sequencing. Biology, 9.","DOI":"10.3390\/biology9090295"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1111\/tpj.15507","article-title":"Multi-omics network-based functional annotation of unknown Arabidopsis genes","volume":"108","author":"Depuydt","year":"2021","journal-title":"Plant J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Castillejo, M.A., Pascual, J., Jorr\u00edn-Novo, J.V., and Balbuena, T.S. (2023). Proteomics research in forest trees: A 2012\u20132022 update. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1130665"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1093\/jxb\/erx333","article-title":"Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants","volume":"69","author":"Syaifullah","year":"2018","journal-title":"J. Exp. Bot."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1093\/jxb\/erae033","article-title":"Multiomics analyses reveal the central role of the nucleolus and its machinery during heat stress acclimation in Pinus radiata","volume":"75","author":"Valledor","year":"2024","journal-title":"J. Exp. Bot."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Sagar, A., K\u00e4rh\u00e4, K., J\u00e4rvelin, K., and Ghabcheloo, R. (2025). Evaluation of Simulation Framework for Detecting the Quality of Forest Tree Stems. Forests, 16.","DOI":"10.3390\/f16061023"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"W476","DOI":"10.1093\/nar\/gkab394","article-title":"OmicsAnalyst: A comprehensive web-based platform for visual analytics of multi-omics data","volume":"49","author":"Zhou","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s13562-026-01048-0","article-title":"AI-assisted integretomics approaches in plant stress research","volume":"35","author":"Singh","year":"2026","journal-title":"J. Plant Biochem. Biotechnol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Murmu, S., Sinha, D., Chaurasia, H., Sharma, S., Das, R., Jha, G.K., and Archak, S. (2024). A review of artificial intelligence-assisted omics techniques in plant defense: Current trends and future directions. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1292054"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Cembrowska-Lech, D., Krzemi\u0144ska, A., Miller, T., Nowakowska, A., Adamski, C., Radaczy\u0144ska, M., Mikiciuk, G., and Mikiciuk, M. (2023). An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture. Biology, 12.","DOI":"10.3390\/biology12101298"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s00299-023-03013-w","article-title":"Abscisic acid in plants under abiotic stress: Crosstalk with major phytohormones","volume":"42","author":"Singh","year":"2023","journal-title":"Plant Cell Rep."},{"key":"ref_79","first-page":"e014","article-title":"Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches","volume":"5","author":"Mummadi","year":"2025","journal-title":"For. Res."},{"key":"ref_80","first-page":"e016","article-title":"When AI meets hormones: Opportunities and challenges of AI for advancements in plant hormone research and agriculture","volume":"1","year":"2025","journal-title":"Plant Horm."},{"key":"ref_81","unstructured":"Zhou, X., Shoji, W., and Robert, M. (November, January 27). Metabolic activity within Escherichia coli macrocolonies and biofilms. Proceedings of the 19th International Conference on Systems Biology (ICSB 2018), Lyon, France."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1007\/s00253-022-11963-6","article-title":"Machine learning: Its challenges and opportunities in plant system biology","volume":"106","author":"Hesami","year":"2022","journal-title":"Appl. Microbiol. Biotechnol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s41060-024-00509-w","article-title":"A review of random forest-based feature selection methods for data science education and applications","volume":"20","author":"Iranzad","year":"2025","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wu, C., Wang, Y., and Sun, H. (2022). Targeted and untargeted metabolomics reveals deep analysis of drought stress responses in needles and roots of Pinus taeda seedlings. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.1031466"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhao, Y., Huang, Y., and Liu, J. (2025). Integrating Drought Stress Signaling and Smart Breeding for Climate-Resilient Crops: Regulatory Mechanisms and Genetic Strategies. Plants, 14.","DOI":"10.3390\/plants14243714"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Rico-Ch\u00e1vez, A.K., Franco, J.A., Fernandez-Jaramillo, A.A., Contreras-Medina, L.M., Guevara-Gonz\u00e1lez, R.G., and Hernandez-Escobedo, Q. (2022). Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management. Plants, 11.","DOI":"10.3390\/plants11070970"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Gai, Y., Liu, S., Zhang, Z., Wei, J., Wang, H., Liu, L., Bai, Q., Qin, Q., Zhao, C., and Zhang, S. (2025). Integrative approaches to soybean resilience, productivity, and utility: A review of genomics, computational modeling, and economic viability. Plants, 14.","DOI":"10.3390\/plants14050671"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"20240252","DOI":"10.1098\/rstb.2024.0252","article-title":"Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops","volume":"380","author":"Singhal","year":"2025","journal-title":"Philos. Trans. B"},{"key":"ref_89","first-page":"75","article-title":"Harnessing machine learning approach for phenotyping and deciphering the plants biotic and abiotic stresses responsive molecular mechanisms","volume":"13","author":"Panahi","year":"2024","journal-title":"Crop Biotechnol."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Song, F., Zhou, J., Quan, M., Xiao, L., Lu, W., Qin, S., Fang, Y., Wang, D., Li, P., and Du, Q. (2022). Transcriptome and association mapping revealed functional genes respond to drought stress in Populus. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.829888"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1038\/s41598-023-27746-6","article-title":"Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species","volume":"13","author":"Tahmasebi","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Yaschenko, A.E., Alonso, J.M., and Stepanova, A.N. (2026). Synthetic promoter design in plants: Integration of computational and experimental approaches. Front. Plant Sci., 17.","DOI":"10.3389\/fpls.2026.1768521"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1111\/pbi.13299","article-title":"Learning from methylomes: Epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation","volume":"18","author":"Champigny","year":"2020","journal-title":"Plant Biotechnol. J."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Pervaiz, T., Liu, S.W., Uddin, S., Amjid, M.W., Niu, S.H., and Wu, H.X. (2021). The Transcriptional Landscape and Hub Genes Associated with Physiological Responses to Drought Stress in Pinus tabuliformis. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22179604"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Nie, R., Wu, C., Ji, X., Li, A., Zheng, X., Tang, J., Sun, L., Su, Y., and Zhang, J. (2025). Methyl Jasmonate Orchestrates Multi-Pathway Antioxidant Defense to Enhance Salt Stress Tolerance in Walnut (Juglans regia L.). Antioxidants, 14.","DOI":"10.3390\/antiox14080974"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13007-024-01158-7","article-title":"Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network","volume":"20","author":"Chang","year":"2024","journal-title":"Plant Methods"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, D., Chen, P., Zhang, C., Yao, S., Hao, Q., Agassin, R.H., and Ji, K. (2023). The Transcriptomic Analysis of the Response of Pinus massoniana to Drought Stress and a Functional Study on the ERF1 Transcription Factor. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms241311103"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, X., Liu, S., Zhang, Z., Yu, Y., Wu, T., Chen, Y., Chen, Y., Meng, X., and Li, W. (2026). Integrated transcriptomic and metabolomic analysis of provenances and organ-specific responses to drought and rehydration in Pinus tabuliformis. Front. Plant Sci., 17.","DOI":"10.3389\/fpls.2026.1778743"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Li, L., Li, Y., and Ding, G. (2024). Response mechanism of carbon metabolism of Pinus massoniana to gradient high temperature and drought stress. BMC Genomics, 25.","DOI":"10.1186\/s12864-024-10054-2"},{"key":"ref_100","unstructured":"Armoush, A. (2026, July 03). Dimensionality Reduction and Machine Learning for Genome-Wide Association Studies in Norway Spruce: A Comparative Study of XGBoost and CatBoost. Available online: https:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2:2001270."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Nazari, L., Ghotbi, V., Nadimi, M., and Paliwal, J. (2023). A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis. Algorithms, 16.","DOI":"10.3390\/a16090407"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2417560","DOI":"10.1002\/advs.202417560","article-title":"Unveiling salt tolerance mechanisms in plants: Integrating the KANMB machine learning model with metabolomic and transcriptomic analysis","volume":"12","author":"Chen","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Mehdizadeh Hakkak, M., and Tohidfar, M. (2026). Integrative cross-platform transcriptomic analysis coupled with machine learning identifies candidate biomarkers of abiotic stress resilience in Arabidopsis thaliana. BMC Plant Biol., 26.","DOI":"10.1186\/s12870-026-08451-8"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"bbab454","DOI":"10.1093\/bib\/bbab454","article-title":"A roadmap for multi-omics data integration using deep learning","volume":"23","author":"Kang","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1038\/s41586-025-08941-z","article-title":"Single-cell transcriptomics reveal how root tissues adapt to soil stress","volume":"642","author":"Zhu","year":"2025","journal-title":"Nature"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"111608","DOI":"10.1016\/j.ecolind.2024.111608","article-title":"A deep-learning-based tree species classification for natural secondary forests using unmanned aerial vehicle hyperspectral images and LiDAR","volume":"159","author":"Ma","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_107","unstructured":"Bustos, J.P.R. (2024). Application of DEEP LEARNING to the Processing of TERRESTRIAL LiDAR Data for the Evaluation of ARCHITECTURAL FEATURES and Functioning of Fruit Trees. [Ph.D. Thesis, Universit\u00e9 de Montpellier]."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Aghadinuno, C., Ismail, Y., Dad, F., El Dakkak, E., Qi, Y., Gray, W., Luo, J., and Lacy, F. (2025). Application of Convolutional and Recurrent Neural Networks in Classifying Plant Responses to Abiotic Stress. Appl. Sci., 15.","DOI":"10.3390\/app152010960"},{"key":"ref_109","unstructured":"van Bree, R., Marcos, D., and Athanasiadis, I.N. (March, January 25). Hybrid phenology modeling for predicting temperature effects on tree dormancy. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Mufassirin, M.M., and Amath, A.A.S. (2026). Advanced Feature Selection and Extraction Techniques for Omics Data Analysis: Applications in Multi-Omics Integration. Feature Selection and Feature Extraction on Omics Data, Chapman and Hall\/CRC.","DOI":"10.1201\/9781003591658-2"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Al-Hurani, I. (2026, July 03). An Autoencoder and Generative Adversarial Networks Framework for Multi-Omics Data Analysis. Available online: https:\/\/knowledgecommons.lakeheadu.ca\/handle\/2453\/5619.","DOI":"10.7490\/f1000research.1120071.1"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"108813","DOI":"10.1016\/j.compbiomed.2024.108813","article-title":"Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data","volume":"179","author":"Zhao","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Mandal, S.S. (2017). Gene Regulation, Epigenetics and Hormone Signaling, John Wiley & Sons.","DOI":"10.1002\/9783527697274"},{"key":"ref_114","unstructured":"Yordanov, A. (2026, July 03). A Regression-Based Approach to Estimating Accuracy in Graph Neural Networks. Available online: https:\/\/hdl.handle.net\/2117\/416550."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Bao Ngo, T.G., Liseron-Monfils, C., Das, S., Ubbens, J., Ashe, P., and Konkin, D. (2025). Improving plant functional annotation from knowledge graphs using Graph Neural Networks. bioRxiv.","DOI":"10.1101\/2025.05.07.652750"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"bbaf109","DOI":"10.1093\/bib\/bbaf109","article-title":"Graph neural networks for single-cell omics data: A review of approaches and applications","volume":"26","author":"Li","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"2415106","DOI":"10.1002\/advs.202415106","article-title":"Hierarchical regulatory networks reveal conserved drivers of plant drought response at the cell-type level","volume":"12","author":"Liu","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"bbag008","DOI":"10.1093\/bib\/bbag008","article-title":"Predicting protein\u2013carbohydrate binding sites: A deep learning approach integrating protein language model embeddings and structural features","volume":"27","year":"2026","journal-title":"Brief. Bioinform."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Yetgin, A., Srivastava, R.K., and Mandal, N. (2025). Insights into Plant Hormone Signaling Networks for Environmental Responses. Mitigation and Adaptation Strategies Against Climate Change in Natural Systems, Springer.","DOI":"10.1007\/978-3-031-75968-0_26"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"102662","DOI":"10.1016\/j.pbi.2024.102662","article-title":"Gene regulatory networks in abiotic stress responses via single-cell sequencing and spatial technologies: Advances and opportunities","volume":"82","author":"Jain","year":"2024","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Zhao, W., Chen, X., Wang, J., Cheng, Z., Ma, X., Zheng, Q., Xu, Z., and Zhang, F. (2025). Emerging Mechanisms of Plant Responses to Abiotic Stress. Plants, 14.","DOI":"10.3390\/plants14223445"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, X., Li, S., Xu, G., Ou, X., Wang, S., Zhou, K., and Yang, J. (2026). Metabolic Landscape and Core Regulatory Network of Monocotyledonous and Dicotyledonous Plants in Drought Response Based on Multi-Omics. Plants, 15.","DOI":"10.3390\/plants15020299"}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/14\/2185\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T01:13:37Z","timestamp":1784250817000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/14\/2185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":124,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["plants15142185"],"URL":"https:\/\/doi.org\/10.3390\/plants15142185","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]}}}