{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T09:08:03Z","timestamp":1789031283653,"version":"build-2803163510"},"reference-count":204,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ag-Eng"},{"name":"Institute of Physical Artificial Intelligence Postdoctoral Fellowship"},{"DOI":"10.13039\/100005825","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["1013620"],"award-info":[{"award-number":["1013620"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.<\/jats:p>","DOI":"10.1093\/jxb\/erag323","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:51:02Z","timestamp":1788954662000},"source":"Crossref","is-referenced-by-count":0,"title":["Decoding gene regulation in plant genomes with artificial intelligence"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6597-4955","authenticated-orcid":false,"given":"Niraj","family":"Rayamajhi","sequence":"first","affiliation":[{"name":"Department of Horticulture and Landscape Architecture, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]},{"name":"Center for Plant Biology, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianci","family":"Liu","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Gao","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1051-6636","authenticated-orcid":false,"given":"Kranthi","family":"Varala","sequence":"additional","affiliation":[{"name":"Department of Horticulture and Landscape Architecture, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]},{"name":"Center for Plant Biology, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5258-7355","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Horticulture and Landscape Architecture, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]},{"name":"Center for Plant Biology, Purdue University , West Lafayette, IN 47907 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"2026091004222357100_erag323-B1","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf468","article-title":"Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions","volume":"26","author":"Aghziel","year":"2025","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B2","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1038\/s41586-025-08854-x","article-title":"Universal photonic artificial intelligence acceleration","volume":"640","author":"Ahmed","year":"2025","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B3","author":"Akpokiro","year":"2021"},{"key":"2026091004222357100_erag323-B4","doi-asserted-by":"crossref","DOI":"10.1186\/s12859-022-04971-w","article-title":"EnsembleSplice: ensemble deep learning model for splice site prediction","volume":"23","author":"Akpokiro","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"2026091004222357100_erag323-B5","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1016\/j.csbj.2023.05.031","article-title":"CNNSplice: robust models for splice site prediction using convolutional neural networks","volume":"21","author":"Akpokiro","year":"2023","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B6","doi-asserted-by":"crossref","first-page":"138203","DOI":"10.1109\/ACCESS.2020.3002995","article-title":"A CNN-based RNA N6-methyladenosine site predictor for multiple Species using heterogeneous features representation","volume":"8","author":"Alam","year":"2020","journal-title":"IEEE Access"},{"key":"2026091004222357100_erag323-B7","doi-asserted-by":"crossref","DOI":"10.3390\/genes12081117","article-title":"i4mC-deep: an intelligent predictor of N4-methylcytosine sites using a deep learning approach with chemical properties","volume":"12","author":"Alam","year":"2021","journal-title":"Genes"},{"key":"2026091004222357100_erag323-B8","doi-asserted-by":"crossref","DOI":"10.1016\/j.gene.2020.100035","article-title":"Splice2Deep: an ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA","volume":"763","author":"Albaradei","year":"2020","journal-title":"Gene"},{"key":"2026091004222357100_erag323-B9","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nature Biotechnology"},{"key":"2026091004222357100_erag323-B10","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","article-title":"Effective gene expression prediction from sequence by integrating long-range interactions","volume":"18","author":"Avsec","year":"2021","journal-title":"Nature Methods"},{"key":"2026091004222357100_erag323-B11","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1038\/s41586-025-10014-0","article-title":"Advancing regulatory variant effect prediction with AlphaGenome","volume":"649","author":"Avsec","year":"2026","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B12","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1038\/s41576-025-00841-2","article-title":"Predicting gene expression from DNA sequence using deep learning models","volume":"26","author":"Barbadilla-Mart\u00ednez","year":"2025","journal-title":"Nature Reviews: Genetics"},{"key":"2026091004222357100_erag323-B13","article-title":"m5c-iEnsem: 5-methylcytosine sites identification through ensemble models","volume":"41","author":"Bilal","year":"2025","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"2026091004222357100_erag323-B15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/335191.335388","article-title":"LOF: identifying density-based local outliers","volume":"29","author":"Breunig","year":"2000","journal-title":"SIGMOD Record"},{"key":"2026091004222357100_erag323-B16","doi-asserted-by":"crossref","first-page":"457","DOI":"10.2174\/1574893615999200707142852","article-title":"Deep-BSC: predicting raw DNA binding pattern in Arabidopsis Thaliana","volume":"16","author":"Bukhari","year":"2021","journal-title":"Current Bioinformatics"},{"key":"2026091004222357100_erag323-B17","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine learning for molecular and materials science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B18","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1038\/nmeth.4526","article-title":"Machine learning: a primer","volume":"14","author":"Bzdok","year":"2017","journal-title":"Nature Methods"},{"key":"2026091004222357100_erag323-B19","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nature Methods"},{"key":"2026091004222357100_erag323-B20","doi-asserted-by":"crossref","DOI":"10.1093\/gpbjnl\/qzaf024","article-title":"scPlantLLM: a foundation model for exploring single-cell expression atlases in plants","volume":"23","author":"Cao","year":"2025","journal-title":"Genomics, Proteomics & Bioinformatics"},{"key":"2026091004222357100_erag323-B21","first-page":"1027","article-title":"Staem5: a novel computational approach for accurate prediction of m5C site","volume":"26","author":"Chai","year":"2021","journal-title":"Molecular Therapy: Nucleic Acids"},{"key":"2026091004222357100_erag323-B22","doi-asserted-by":"crossref","first-page":"108026","DOI":"10.1016\/j.compbiomed.2024.108026","article-title":"DEPICTER: deep representation clustering for histology annotation","volume":"170","author":"Chelebian","year":"2024","journal-title":"Computers in Biology and Medicine"},{"key":"2026091004222357100_erag323-B23","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1186\/s12859-020-03828-4","article-title":"m5CPred-SVM: a novel method for predicting m5C sites of RNA","volume":"21","author":"Chen","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"2026091004222357100_erag323-B24","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1175837","article-title":"TSPTFBS 2.0: trans-species prediction of transcription factor binding sites and identification of their core motifs in plants","volume":"14","author":"Cheng","year":"2023","journal-title":"Frontiers in Plant Science"},{"key":"2026091004222357100_erag323-B25","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.pbi.2008.12.006","article-title":"Epigenetic regulation of stress responses in plants","volume":"12","author":"Chinnusamy","year":"2009","journal-title":"Current Opinion in Plant Biology"},{"key":"2026091004222357100_erag323-B26","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3115\/v1\/W14-4012","volume-title":"Proceedings of SSST-8, eighth workshop on syntax, semantics and structure in statistical translation","author":"Cho","year":"2014"},{"key":"2026091004222357100_erag323-B27","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/B978-0-323-95359-7.00002-9","volume-title":"Artificial intelligence in pathology","author":"Cohen","year":"2025","edition":"2"},{"key":"2026091004222357100_erag323-B28","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2024.105531","article-title":"Improving the quality evaluation process of machine learning algorithms applied to landslide time series analysis","volume":"184","author":"Conciatori","year":"2024","journal-title":"Computers & Geosciences"},{"key":"2026091004222357100_erag323-B29","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1038\/337129a0","article-title":"The recent excitement about neural networks","volume":"337","author":"Crick","year":"1989","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B30","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MM.2021.3113475","article-title":"Evolution of the graphics processing unit (GPU)","volume":"41","author":"Dally","year":"2021","journal-title":"IEEE Micro"},{"key":"2026091004222357100_erag323-B31","doi-asserted-by":"crossref","first-page":"100455","DOI":"10.1016\/j.xplc.2022.100455","article-title":"iCREPCP: a deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters","volume":"4","author":"Deng","year":"2023","journal-title":"Plant Communications"},{"key":"2026091004222357100_erag323-B32","first-page":"4171","volume-title":"Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, volume 1 (Long and short papers)","author":"Devlin","year":"2019"},{"key":"2026091004222357100_erag323-B33","first-page":"332","article-title":"Prediction of m5C modifications in RNA sequences by combining multiple sequence features","volume":"21","author":"Dou","year":"2020","journal-title":"Molecular Therapy: Nucleic Acids"},{"key":"2026091004222357100_erag323-B34","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1126\/science.6262918","article-title":"5-Methylcytosine in Eukaryotic DNA","volume":"212","author":"Ehrlich","year":"1981","journal-title":"Science"},{"key":"2026091004222357100_erag323-B35","author":"Ester","year":"1996"},{"key":"2026091004222357100_erag323-B36","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1016\/j.hpj.2025.08.004","article-title":"Deep learning applications advance plant genomics research","volume":"11","author":"Fan","year":"2025","journal-title":"Horticultural Plant Journal"},{"key":"2026091004222357100_erag323-B37","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/TCBB.2024.3421228","article-title":"Ense-i6mA: identification of DNA N6-methyladenine sites using XGB-RFE feature selection and ensemble machine learning","volume":"21","author":"Fan","year":"2024","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"2026091004222357100_erag323-B38","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1002\/qub2.26","article-title":"Gene regulatory network inference based on causal discovery integrating with graph neural network","volume":"11","author":"Feng","year":"2023","journal-title":"Quantitative Biology"},{"key":"2026091004222357100_erag323-B39","doi-asserted-by":"crossref","first-page":"8689","DOI":"10.1073\/pnas.1002720107","article-title":"Conservation and divergence of methylation patterning in plants and animals","volume":"107","author":"Feng","year":"2010","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B40","doi-asserted-by":"crossref","first-page":"100010","DOI":"10.1016\/j.ncrops.2023.12.005","article-title":"AI breeder: genomic predictions for crop breeding","volume":"1","author":"Feng","year":"2024","journal-title":"New Crops"},{"key":"2026091004222357100_erag323-B41","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1038\/s41586-024-08391-z","article-title":"A foundation model of transcription across human cell types","volume":"637","author":"Fu","year":"2025","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B42","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/978-3-031-92387-6_9","volume-title":"Computer vision\u2014ECCV 2024 workshops","author":"Gao","year":"2025"},{"key":"2026091004222357100_erag323-B43","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2103070118","article-title":"Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function","volume":"118","author":"Gardiner","year":"2021","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B44","doi-asserted-by":"crossref","DOI":"10.1016\/j.intell.2024.101832","article-title":"Defining intelligence: bridging the gap between human and artificial perspectives","volume":"104","author":"Gignac","year":"2024","journal-title":"Intelligence"},{"key":"2026091004222357100_erag323-B45","volume-title":"Deep learning: a visual approach","author":"Glassner","year":"2021"},{"key":"2026091004222357100_erag323-B46","volume-title":"Deep learning","author":"Goodfellow","year":"2016"},{"key":"2026091004222357100_erag323-B47","author":"Graves","year":"2005"},{"key":"2026091004222357100_erag323-B48","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nature Reviews: Molecular Cell Biology"},{"key":"2026091004222357100_erag323-B49","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.jgg.2022.02.012","article-title":"SMOC: a smart model for open chromatin region prediction in rice genomes","volume":"49","author":"Guo","year":"2022","journal-title":"Journal of Genetics and Genomics"},{"key":"2026091004222357100_erag323-B50","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae324","article-title":"PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants","volume":"25","author":"Gupta","year":"2024","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B51","doi-asserted-by":"crossref","DOI":"10.1016\/j.xplc.2025.101543","article-title":"PTF-V\u0101c: an explainable and generative deep co-learning encoder\u2013decoder system for ab initio discovery of plant transcription factor binding sites","volume":"6","author":"Gupta","year":"2025","journal-title":"Plant Communications"},{"key":"2026091004222357100_erag323-B52","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf579","article-title":"DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants","volume":"26","author":"Gupta","year":"2025","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B53","doi-asserted-by":"crossref","first-page":"1206139","DOI":"10.3389\/fdata.2023.1206139","article-title":"Artificial intelligence research strategy of the United States: critical assessment and policy recommendations","volume":"6","author":"Gursoy","year":"2023","journal-title":"Frontiers in Big Data"},{"key":"2026091004222357100_erag323-B54","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-3-031-91705-9_2","volume-title":"Using AI in academic writing and research: a complete guide to effective and ethical academic AI","author":"Haber","year":"2025"},{"key":"2026091004222357100_erag323-B55","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1038\/s41576-022-00526-0","article-title":"The spatial organization of transcriptional control","volume":"24","author":"Hafner","year":"2023","journal-title":"Nature Reviews: Genetics"},{"key":"2026091004222357100_erag323-B56","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac514","article-title":"A review of methods for predicting DNA N6-methyladenine sites","volume":"24","author":"Han","year":"2023","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B57","doi-asserted-by":"crossref","first-page":"83","DOI":"10.12779\/dnd.2018.17.3.83","article-title":"Artificial neural network: understanding the basic concepts without mathematics","volume":"17","author":"Han","year":"2018","journal-title":"Dementia and Neurocognitive Disorders"},{"key":"2026091004222357100_erag323-B58","first-page":"100","article-title":"Algorithm AS 136: a K-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"Journal of the Royal Statistical Society: Series C, Applied Statistics"},{"key":"2026091004222357100_erag323-B59","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1016\/j.ijbiomac.2019.12.009","article-title":"i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome","volume":"157","author":"Hasan","year":"2020","journal-title":"International Journal of Biological Macromolecules"},{"key":"2026091004222357100_erag323-B60","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa202","article-title":"Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework","volume":"22","author":"Hasan","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B61","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1146\/annurev-arplant-042110-103806","article-title":"The epigenome and plant development","volume":"62","author":"He","year":"2011","journal-title":"Annual Review of Plant Biology"},{"key":"2026091004222357100_erag323-B62","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf470","article-title":"Machine learning methods for gene regulatory network inference","volume":"26","author":"Hegde","year":"2025","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B63","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nrm.2017.63","article-title":"Splicing and transcription touch base: co-transcriptional spliceosome assembly and function","volume":"18","author":"Herzel","year":"2017","journal-title":"Nature Reviews: Molecular Cell Biology"},{"key":"2026091004222357100_erag323-B64","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"2026091004222357100_erag323-B65","doi-asserted-by":"crossref","DOI":"10.1186\/s13040-023-00348-8","article-title":"6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site","volume":"16","author":"Huang","year":"2023","journal-title":"BioData Mining"},{"key":"2026091004222357100_erag323-B66","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Huynh-Thu","year":"2010","journal-title":"PLoS One"},{"key":"2026091004222357100_erag323-B67","doi-asserted-by":"crossref","DOI":"10.3390\/genes12081155","article-title":"bCNN-methylpred: feature-based prediction of RNA sequence modification using branch convolutional neural network","volume":"12","author":"Islam","year":"2021","journal-title":"Genes"},{"key":"2026091004222357100_erag323-B68","doi-asserted-by":"crossref","DOI":"10.1016\/j.ygeno.2025.111037","article-title":"Feadm5C: enhancing prediction of RNA 5-Methylcytosine modification sites with physicochemical molecular graph features","volume":"117","author":"Jiang","year":"2025","journal-title":"Genomics"},{"key":"2026091004222357100_erag323-B69","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behavior Therapy"},{"key":"2026091004222357100_erag323-B70","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/978-0-387-30164-8_425","volume-title":"Encyclopedia of machine learning","author":"Jin","year":"2011"},{"key":"2026091004222357100_erag323-B71","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"2026091004222357100_erag323-B72","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1038\/s41477-021-00932-y","article-title":"Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters","volume":"7","author":"Jores","year":"2021","journal-title":"Nature Plants"},{"key":"2026091004222357100_erag323-B73","author":"Jouppi","year":"2017"},{"key":"2026091004222357100_erag323-B74","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e39140","article-title":"Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery","volume":"10","author":"Jyoti","year":"2024","journal-title":"Heliyon"},{"key":"2026091004222357100_erag323-B75","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns"},{"key":"2026091004222357100_erag323-B76","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1101\/gr.227819.117","article-title":"Sequential regulatory activity prediction across chromosomes with convolutional neural networks","volume":"28","author":"Kelley","year":"2018","journal-title":"Genome Research"},{"key":"2026091004222357100_erag323-B77","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1016\/j.csbj.2021.03.015","article-title":"Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation","volume":"19","author":"Khanal","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B78","doi-asserted-by":"crossref","DOI":"10.1016\/j.ygeno.2022.110384","article-title":"iProm-Zea: a two-layer model to identify plant promoters and their types using convolutional neural network","volume":"114","author":"Kim","year":"2022","journal-title":"Genomics"},{"key":"2026091004222357100_erag323-B79","doi-asserted-by":"crossref","DOI":"10.1016\/j.mocell.2025.100273","article-title":"Identification and application of bioparts for plant synthetic biology","volume":"48","author":"Koo","year":"2025","journal-title":"Molecules and Cells"},{"key":"2026091004222357100_erag323-B80","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2115640118","article-title":"Green plant genomes: what we know in an era of rapidly expanding opportunities","volume":"119","author":"Kress","year":"2022","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B81","doi-asserted-by":"crossref","DOI":"10.1186\/gb-2013-14-6-123","article-title":"Gene regulatory networks in plants: learning causality from time and perturbation","volume":"14","author":"Krouk","year":"2013","journal-title":"Genome Biology"},{"key":"2026091004222357100_erag323-B82","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13152582","article-title":"What is machine learning, artificial neural networks and deep learning? \u2014 Examples of practical applications in medicine","volume":"13","author":"Kufel","year":"2023","journal-title":"Diagnostics"},{"key":"2026091004222357100_erag323-B83","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.tplants.2024.04.013","article-title":"Large language models in plant biology","volume":"29","author":"Lam","year":"2024","journal-title":"Trends in Plant Science"},{"key":"2026091004222357100_erag323-B84","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B85","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/nmeth.3968","article-title":"Points of Significance: model selection and overfitting","volume":"13","author":"Lever","year":"2016","journal-title":"Nature Methods"},{"key":"2026091004222357100_erag323-B86","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107030","article-title":"EpiTEAmDNA: sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species","volume":"160","author":"Li","year":"2023","journal-title":"Computers in Biology and Medicine"},{"key":"2026091004222357100_erag323-B87","article-title":"HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species","volume":"13","author":"Li","year":"2025","journal-title":"PeerJ"},{"key":"2026091004222357100_erag323-B88","doi-asserted-by":"crossref","DOI":"10.1016\/j.xplc.2025.101392","article-title":"Chromatin accessibility dynamics and transcriptional regulatory networks underlying the primary nitrogen response in rice roots","volume":"6","author":"Li","year":"2025","journal-title":"Plant Communications"},{"key":"2026091004222357100_erag323-B89","author":"Li","year":"2022"},{"key":"2026091004222357100_erag323-B90","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1186\/s12859-022-04789-6","article-title":"Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction","volume":"23","author":"Liang","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"2026091004222357100_erag323-B91","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.devcel.2018.03.012","article-title":"DNA N6-adenine methylation in Arabidopsis thaliana","volume":"45","author":"Liang","year":"2018","journal-title":"Developmental Cell"},{"key":"2026091004222357100_erag323-B92","doi-asserted-by":"crossref","DOI":"10.3390\/sym13050899","article-title":"iRG-4mC: neural network based tool for identification of DNA 4mC sites in Rosaceae genome","volume":"13","author":"Lim","year":"2021","journal-title":"Symmetry"},{"key":"2026091004222357100_erag323-B93","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1038\/s41588-024-02053-6","article-title":"Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation","volume":"57","author":"Linder","year":"2025","journal-title":"Nature Genetics"},{"key":"2026091004222357100_erag323-B94","doi-asserted-by":"crossref","first-page":"5160","DOI":"10.1093\/bioinformatics\/btac671","article-title":"MSNet-4mC: learning effective multi-scale representations for identifying DNA N4-methylcytosine sites","volume":"38","author":"Liu","year":"2022","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B95","author":"Liu","year":"2008"},{"key":"2026091004222357100_erag323-B96","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.molp.2024.12.006","article-title":"PDLLMs: a group of tailored DNA large language models for analyzing plant genomes","volume":"18","author":"Liu","year":"2025","journal-title":"Molecular Plant"},{"key":"2026091004222357100_erag323-B97","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1093\/bioinformatics\/btaa1100","article-title":"TSPTFBS: a Docker image for trans-species prediction of transcription factor binding sites in plants","volume":"37","author":"Liu","year":"2021","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B98","article-title":"Multi-kernel feature extraction with dynamic fusion and downsampled residual feature embedding for predicting rice RNA N6-methyladenine sites","volume":"26","author":"Liu","year":"2025","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B99","article-title":"DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites","volume":"22","author":"Liu","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B100","article-title":"DRANetSplicer: a splice site prediction model based on deep residual attention networks","volume":"15","author":"Liu","year":"2024","journal-title":"Genes"},{"key":"2026091004222357100_erag323-B101","article-title":"m5Cpred-XS: a new method for predicting RNA m5C sites based on XGBoost and SHAP","volume":"13","author":"Liu","year":"2022","journal-title":"Frontiers in Genetics"},{"key":"2026091004222357100_erag323-B102","doi-asserted-by":"crossref","DOI":"10.1038\/s41438-019-0160-4","article-title":"MDR: an integrative DNA N6-methyladenine and N4-methylcytosine modification database for Rosaceae","volume":"6","author":"Liu","year":"2019","journal-title":"Horticulture Research"},{"key":"2026091004222357100_erag323-B103","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2024.101046","article-title":"Avoiding common machine learning pitfalls","volume":"5","author":"Lones","year":"2024","journal-title":"Patterns"},{"key":"2026091004222357100_erag323-B104","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-89010-0","volume-title":"Multivariate statistical machine learning methods for genomic prediction (Chapter 4)","author":"L\u00f3pez","year":"2022"},{"key":"2026091004222357100_erag323-B105","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.ymeth.2025.02.001","article-title":"HybProm: an attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter","volume":"235","author":"Luo","year":"2025","journal-title":"Methods"},{"key":"2026091004222357100_erag323-B106","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1093\/bib\/bbz048","article-title":"Evaluation of different computational methods on 5-methylcytosine sites identification","volume":"21","author":"Lv","year":"2020","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B107","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.cels.2016.07.001","article-title":"DNA shape features improve transcription factor binding site predictions in vivo","volume":"3","author":"Mathelier","year":"2016","journal-title":"Cell Systems"},{"key":"2026091004222357100_erag323-B108","first-page":"12","article-title":"A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955","volume":"27","author":"McCarthy","year":"2006","journal-title":"AI Magazine"},{"key":"2026091004222357100_erag323-B109","doi-asserted-by":"crossref","DOI":"10.21105\/joss.00861","article-title":"UMAP: uniform manifold approximation and projection","volume":"3","author":"McInnes","year":"2018","journal-title":"Journal of Open Source Software"},{"key":"2026091004222357100_erag323-B110","doi-asserted-by":"crossref","DOI":"10.1038\/s42003-024-06465-2","article-title":"A foundational large language model for edible plant genomes","volume":"7","author":"Mendoza-Revilla","year":"2024","journal-title":"Communications Biology"},{"key":"2026091004222357100_erag323-B111","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2026330118","article-title":"Predicting transcriptional responses to cold stress across plant species","volume":"118","author":"Meng","year":"2021","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B112","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1093\/bioinformatics\/bty916","article-title":"GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks","volume":"35","author":"Moerman","year":"2019","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B113","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.1016\/j.csbj.2021.07.021","article-title":"A primer on machine learning techniques for genomic applications","volume":"19","author":"Monaco","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B114","first-page":"3","article-title":"The dartmouth college artificial intelligence conference: the next fifty years","volume":"27","author":"Moor","year":"2006","journal-title":"AI Magazine"},{"key":"2026091004222357100_erag323-B115","doi-asserted-by":"crossref","DOI":"10.1186\/s13040-021-00237-y","article-title":"DASSI: differential architecture search for splice identification from DNA sequences","volume":"14","author":"Moosa","year":"2021","journal-title":"BioData Mining"},{"key":"2026091004222357100_erag323-B116","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105627","article-title":"CapsProm: a capsule network for promoter prediction","volume":"147","author":"Moraes","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"2026091004222357100_erag323-B117","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TCBB.2023.3339597","article-title":"promSEMBLE: hard pattern mining and ensemble learning for detecting DNA promoter sequences","volume":"21","author":"Nagda","year":"2024","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"2026091004222357100_erag323-B118","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2307\/2344614","article-title":"Generalized linear models","volume":"135","author":"Nelder","year":"1972","journal-title":"Journal of the Royal Statistical Society: Series A (General)"},{"key":"2026091004222357100_erag323-B119","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1016\/j.cell.2016.04.038","article-title":"Cistrome and epicistrome features shape the regulatory DNA landscape","volume":"165","author":"O\u2019Malley","year":"2016","journal-title":"Cell"},{"key":"2026091004222357100_erag323-B120","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s42256-022-00463-x","article-title":"The transformational role of GPU computing and deep learning in drug discovery","volume":"4","author":"Pandey","year":"2022","journal-title":"Nature Machine Intelligence"},{"key":"2026091004222357100_erag323-B121","doi-asserted-by":"publisher","author":"Peleke","year":"2025","DOI":"10.21203\/rs.3.rs-6196739\/v1"},{"key":"2026091004222357100_erag323-B122","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-024-47744-0","article-title":"Deep learning the cis-regulatory code for gene expression in selected model plants","volume":"15","author":"Peleke","year":"2024","journal-title":"Nature Communications"},{"key":"2026091004222357100_erag323-B123","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkw226","article-title":"Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences","volume":"44","author":"Quang","year":"2016","journal-title":"Nucleic Acids Research"},{"key":"2026091004222357100_erag323-B124","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1023\/A:1022643204877","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Machine Learning"},{"key":"2026091004222357100_erag323-B125","author":"Radford","year":"2018"},{"key":"2026091004222357100_erag323-B126","author":"Raina","year":"2009"},{"key":"2026091004222357100_erag323-B127","doi-asserted-by":"crossref","DOI":"10.3390\/computers14030093","article-title":"Machine learning and deep learning paradigms: from techniques to practical applications and research frontiers","volume":"14","author":"Razzaq","year":"2025","journal-title":"Computers"},{"key":"2026091004222357100_erag323-B128","doi-asserted-by":"crossref","first-page":"17779","DOI":"10.1109\/ACCESS.2021.3054361","article-title":"m6A-NeuralTool: convolution neural tool for RNA N6-methyladenosine site identification in different Species","volume":"9","author":"Rehman","year":"2021","journal-title":"IEEE Access"},{"key":"2026091004222357100_erag323-B129","doi-asserted-by":"crossref","first-page":"6009","DOI":"10.1016\/j.csbj.2021.10.034","article-title":"DCNN-4mC: densely connected neural network based N4-methylcytosine site prediction in multiple species","volume":"19","author":"Rehman","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B130","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1093\/bioinformatics\/btac434","article-title":"i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites","volume":"38","author":"Rehman","year":"2022","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B131","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106017","article-title":"AdvanceSplice: integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracy","volume":"92","author":"Reza Rezvan","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"2026091004222357100_erag323-B132","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/d41586-019-02307-y","article-title":"Three pitfalls to avoid in machine learning","volume":"572","author":"Riley","year":"2019","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B133","article-title":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare","volume":"7","author":"Riley","year":"2025","journal-title":"The Lancet: Digital Health"},{"key":"2026091004222357100_erag323-B134","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/d41586-025-00907-5","article-title":"Photonic chips provide a processing boost for AI","volume":"640","author":"Rizzo","year":"2025","journal-title":"Nature"},{"key":"2026091004222357100_erag323-B135","doi-asserted-by":"crossref","first-page":"4884","DOI":"10.1016\/j.csbj.2021.08.028","article-title":"Computational biology approaches for mapping transcriptional regulatory networks","volume":"19","author":"Saint-Andr\u00e9","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B136","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1147\/rd.33.0210","article-title":"Some studies in machine learning using the game of checkers","volume":"3","author":"Samuel","year":"1959","journal-title":"IBM Journal of Research and Development"},{"key":"2026091004222357100_erag323-B137","doi-asserted-by":"crossref","DOI":"10.1186\/s12859-021-04471-3","article-title":"Spliceator: multi-species splice site prediction using convolutional neural networks","volume":"22","author":"Scalzitti","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2026091004222357100_erag323-B138","doi-asserted-by":"crossref","DOI":"10.1093\/nargab\/lqae123","article-title":"GenomicLinks: deep learning predictions of 3D chromatin interactions in the maize genome","volume":"6","author":"Schlegel","year":"2024","journal-title":"NAR Genomics and Bioinformatics"},{"key":"2026091004222357100_erag323-B139","author":"Schlegel","year":"2025"},{"key":"2026091004222357100_erag323-B140","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Networks"},{"key":"2026091004222357100_erag323-B141","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Transactions on Signal Processing"},{"key":"2026091004222357100_erag323-B142","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.tplants.2021.06.016","article-title":"Deep learning-based prediction of TFBSs in plants","volume":"26","author":"Shen","year":"2021","journal-title":"Trends in Plant Science"},{"key":"2026091004222357100_erag323-B143","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1016\/j.gpb.2020.06.021","article-title":"CharPlant: a de novo open chromatin region prediction tool for plant genomes","volume":"19","author":"Shen","year":"2021","journal-title":"Genomics, Proteomics & Bioinformatics"},{"key":"2026091004222357100_erag323-B144","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ymeth.2022.02.009","article-title":"Plant6mA: a predictor for predicting N6-methyladenine sites with lightweight structure in plant genomes","volume":"204","author":"Shi","year":"2022","journal-title":"Methods (San Diego, Calif.)"},{"key":"2026091004222357100_erag323-B145","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1256186","article-title":"MethSemble-6mA: an ensemble-based 6 mA prediction server and its application on promoter region of LBD gene family in Poaceae","volume":"14","author":"Sinha","year":"2023","journal-title":"Frontiers in Plant Science"},{"key":"2026091004222357100_erag323-B146","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2174\/0115748936285544231221113226","article-title":"GB5mCPred: cross-species 5mc site predictor based on bootstrap-based stochastic gradient boosting method for Poaceae","volume":"20","author":"Sinha","year":"2025","journal-title":"Current Bioinformatics"},{"key":"2026091004222357100_erag323-B147","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1093\/plphys\/kiae120","article-title":"PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants","volume":"195","author":"Song","year":"2024","journal-title":"Plant Physiology"},{"key":"2026091004222357100_erag323-B148","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2023.107974","article-title":"4mC-CGRU: identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome","volume":"107","author":"Sultana","year":"2023","journal-title":"Computational Biology and Chemistry"},{"key":"2026091004222357100_erag323-B149","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/6636350","article-title":"iPTT(2 L)-CNN: a two-layer predictor for identifying promoters and their types in plant genomes by convolutional neural network","volume":"2021","author":"Sun","year":"2021","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"2026091004222357100_erag323-B150","doi-asserted-by":"crossref","DOI":"10.1186\/s12859-025-06071-x","article-title":"HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer","volume":"26","author":"Sun","year":"2025","journal-title":"BMC Bioinformatics"},{"key":"2026091004222357100_erag323-B151","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.neunet.2020.05.027","article-title":"Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations","volume":"129","author":"Tahir","year":"2020","journal-title":"Neural Networks"},{"key":"2026091004222357100_erag323-B152","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1038\/s42256-020-0211-4","article-title":"Elucidation of DNA methylation on N6-adenine with deep learning","volume":"2","author":"Tan","year":"2020","journal-title":"Nature Machine Intelligence"},{"key":"2026091004222357100_erag323-B153","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.0030116","article-title":"Machine learning and its applications to biology","volume":"3","author":"Tarca","year":"2007","journal-title":"PLOS Computational Biology"},{"key":"2026091004222357100_erag323-B154","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac053","article-title":"BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches","volume":"23","author":"Tsukiyama","year":"2022","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B155","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.csbj.2022.12.043","article-title":"CNN6mA: interpretable neural network model based on position-specific CNN and cross-interactive network for 6 mA site prediction","volume":"21","author":"Tsukiyama","year":"2023","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B156","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"2026091004222357100_erag323-B157","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2020.101890","article-title":"Machine learning in plant science and plant breeding","volume":"24","author":"van Dijk","year":"2021","journal-title":"iScience"},{"key":"2026091004222357100_erag323-B158","author":"Vapnik","year":"1996"},{"key":"2026091004222357100_erag323-B159","author":"Vaswani","year":"2017"},{"key":"2026091004222357100_erag323-B160","doi-asserted-by":"crossref","DOI":"10.3390\/cells9081756","article-title":"DNC4mC-Deep: identification and analysis of DNA N4-methylcytosine sites based on different encoding schemes by using deep learning","volume":"9","author":"Wahab","year":"2020","journal-title":"Cells"},{"key":"2026091004222357100_erag323-B161","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1080\/15476286.2021.1930729","article-title":"M6A-BiNP: predicting N6-methyladenosine sites based on bidirectional position-specific propensities of polynucleotides and pointwise joint mutual information","volume":"18","author":"Wang","year":"2021","journal-title":"RNA Biology"},{"key":"2026091004222357100_erag323-B162","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/978-981-99-4749-2_38","volume-title":"Advanced intelligent computing technology and applications","author":"Wang","year":"2023"},{"key":"2026091004222357100_erag323-B163","first-page":"154","article-title":"Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture","volume":"24","author":"Wang","year":"2021","journal-title":"Molecular Therapy: Nucleic Acids"},{"key":"2026091004222357100_erag323-B164","article-title":"m6AGE: a predictor for N6-methyladenosine sites identification utilizing sequence characteristics and graph embedding-based geometrical information","volume":"12","author":"Wang","year":"2021","journal-title":"Frontiers in Genetics"},{"key":"2026091004222357100_erag323-B165","article-title":"A successful hybrid deep learning model aiming at promoter identification","volume":"23","author":"Wang","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"2026091004222357100_erag323-B166","article-title":"PromGER: promoter prediction based on graph embedding and ensemble learning for eukaryotic sequence","volume":"14","author":"Wang","year":"2023","journal-title":"Genes"},{"key":"2026091004222357100_erag323-B167","article-title":"A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles","volume":"26","author":"Wang","year":"2025","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B168","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijbiomac.2025.148221","article-title":"A comprehensive review of computational methods for predicting DNA N4-methylcytosine sites","volume":"330","author":"Wang","year":"2025","journal-title":"International Journal of Biological Macromolecules"},{"key":"2026091004222357100_erag323-B169","doi-asserted-by":"crossref","DOI":"10.1111\/tpj.17190","article-title":"Identification, characterization, and design of plant genome sequences using deep learning","volume":"121","author":"Wang","year":"2025","journal-title":"The Plant Journal"},{"key":"2026091004222357100_erag323-B170","doi-asserted-by":"crossref","first-page":"5542","DOI":"10.1073\/pnas.1814551116","article-title":"Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence","volume":"116","author":"Washburn","year":"2019","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B171","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/s41576-021-00434-9","article-title":"Navigating the pitfalls of applying machine learning in genomics","volume":"23","author":"Whalen","year":"2022","journal-title":"Nature Reviews: Genetics"},{"key":"2026091004222357100_erag323-B172","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/978-3-030-74394-9_10","volume-title":"Computational psychometrics: new methodologies for a new generation of digital learning and assessment: with examples in R and Python","author":"Wong","year":"2021"},{"key":"2026091004222357100_erag323-B173","doi-asserted-by":"crossref","DOI":"10.1002\/tpg2.20249","article-title":"Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks","volume":"15","author":"Wrightsman","year":"2022","journal-title":"The Plant Genome"},{"key":"2026091004222357100_erag323-B174","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkaf223","article-title":"Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning","volume":"53","author":"Xie","year":"2025","journal-title":"Nucleic Acids Research"},{"key":"2026091004222357100_erag323-B175","article-title":"Foundation models in plant molecular biology: advances, challenges, and future directions","volume":"16","author":"Xu","year":"2025","journal-title":"Frontiers in Plant Science"},{"key":"2026091004222357100_erag323-B176","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa099","article-title":"Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning","volume":"22","author":"Xu","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B177","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad165","article-title":"STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data","volume":"39","author":"Xu","year":"2023","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B178","doi-asserted-by":"crossref","DOI":"10.1016\/j.xinn.2021.100179","article-title":"Artificial intelligence: a powerful paradigm for scientific research","volume":"2","author":"Xu","year":"2021","journal-title":"The Innovation"},{"key":"2026091004222357100_erag323-B179","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s12539-021-00429-4","article-title":"i6mA-VC: a multi-classifier voting method for the computational identification of DNA N6-methyladenine sites","volume":"13","author":"Xue","year":"2021","journal-title":"Interdisciplinary Sciences, Computational Life Sciences"},{"key":"2026091004222357100_erag323-B180","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac425","article-title":"PlantBind: an attention-based multi-label neural network for predicting plant transcription factor binding sites","volume":"23","author":"Yan","year":"2022","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B181","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijbiomac.2023.123180","article-title":"4mCBERT: a computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies","volume":"231","author":"Yang","year":"2023","journal-title":"International Journal of Biological Macromolecules"},{"key":"2026091004222357100_erag323-B182","doi-asserted-by":"crossref","first-page":"4214","DOI":"10.1016\/j.csbj.2024.11.006","article-title":"iResNetDM: an interpretable deep learning approach for four types of DNA methylation modification prediction","volume":"23","author":"Yang","year":"2024","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2026091004222357100_erag323-B183","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108166","article-title":"DeepSF-4mC: a deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features","volume":"171","author":"Yao","year":"2024","journal-title":"Computers in Biology and Medicine"},{"key":"2026091004222357100_erag323-B184","doi-asserted-by":"crossref","first-page":"D85","DOI":"10.1093\/nar\/gkw950","article-title":"MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing","volume":"45","author":"Ye","year":"2017","journal-title":"Nucleic Acids Research"},{"key":"2026091004222357100_erag323-B185","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1038\/s42256-024-00946-z","article-title":"An interpretable RNA foundation model for exploring functional RNA motifs in plants","volume":"6","author":"Yu","year":"2024","journal-title":"Nature Machine Intelligence"},{"key":"2026091004222357100_erag323-B186","article-title":"Systematic analysis and accurate identification of DNA N4-Methylcytosine sites by deep learning","volume":"13","author":"Yu","year":"2022","journal-title":"Frontiers in Microbiology"},{"key":"2026091004222357100_erag323-B187","doi-asserted-by":"crossref","first-page":"4603","DOI":"10.1093\/bioinformatics\/btab677","article-title":"iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization","volume":"37","author":"Yu","year":"2021","journal-title":"Bioinformatics"},{"key":"2026091004222357100_erag323-B188","doi-asserted-by":"crossref","first-page":"27151","DOI":"10.1073\/pnas.1911536116","article-title":"Deep learning for inferring gene relationships from single-cell expression data","volume":"116","author":"Yuan","year":"2019","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B189","doi-asserted-by":"crossref","DOI":"10.1016\/j.cnsns.2025.109423","article-title":"Long short-term memory and gated recurrent unit networks in model predictive control for multivariable processes","volume":"152","author":"Zarzycki","year":"2026","journal-title":"Communications in Nonlinear Science & Numerical Simulation"},{"key":"2026091004222357100_erag323-B190","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2020.00274","article-title":"Developing a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications","volume":"8","author":"Zeng","year":"2020","journal-title":"Frontiers in Bioengineering and Biotechnology"},{"key":"2026091004222357100_erag323-B191","doi-asserted-by":"crossref","DOI":"10.3389\/fcell.2021.664669","article-title":"4mCPred-MTL: accurate identification of DNA 4mC sites in multiple Species using multi-task deep learning based on multi-head attention mechanism","volume":"9","author":"Zeng","year":"2021","journal-title":"Frontiers in Cell and Developmental Biology"},{"key":"2026091004222357100_erag323-B192","article-title":"MuLan-Methyl\u2014multiple transformer-based language models for accurate DNA methylation prediction","volume":"12","author":"Zeng","year":"2023","journal-title":"GigaScience"},{"key":"2026091004222357100_erag323-B193","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2421738122","article-title":"Cross-species modeling of plant genomes at single-nucleotide resolution using a pretrained DNA language model","volume":"122","author":"Zhai","year":"2025","journal-title":"Proceedings of the National Academy of Sciences, USA"},{"key":"2026091004222357100_erag323-B194","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202503135","article-title":"deepTFBS: improving within- and cross-Species prediction of transcription factor binding using deep multi-task and transfer learning","volume":"12","author":"Zhai","year":"2025","journal-title":"Advanced Science"},{"key":"2026091004222357100_erag323-B195","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1111\/jipb.13423","article-title":"Active DNA demethylation in plants: 20 years of discovery and beyond","volume":"64","author":"Zhang","year":"2022","journal-title":"Journal of Integrative Plant Biology"},{"key":"2026091004222357100_erag323-B196","author":"Zhang","year":"2023"},{"key":"2026091004222357100_erag323-B197","doi-asserted-by":"crossref","first-page":"W523","DOI":"10.1093\/nar\/gkab383","article-title":"PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants","volume":"49","author":"Zhao","year":"2021","journal-title":"Nucleic Acids Research"},{"key":"2026091004222357100_erag323-B198","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2024.108077","article-title":"DeepOCR: a multi-species deep-learning framework for accurate identification of open chromatin regions in livestock","volume":"110","author":"Zhao","year":"2024","journal-title":"Computational Biology and Chemistry"},{"key":"2026091004222357100_erag323-B199","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning\u2013based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nature Methods"},{"key":"2026091004222357100_erag323-B200","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf349","article-title":"GRANet: a graph residual attention network for gene regulatory network inference","volume":"26","author":"Zhou","year":"2025","journal-title":"Briefings in Bioinformatics"},{"key":"2026091004222357100_erag323-B201","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108129","article-title":"PSAC-6mA: 6 mA site identifier using self-attention capsule network based on sequence-positioning","volume":"171","author":"Zhou","year":"2024","journal-title":"Computers in Biology and Medicine"},{"key":"2026091004222357100_erag323-B202","doi-asserted-by":"crossref","first-page":"3975","DOI":"10.1093\/jxb\/erad136","article-title":"The expanding role of RNA modifications in plant RNA polymerase II transcripts: highlights and perspectives","volume":"74","author":"Zimna","year":"2023","journal-title":"Journal of Experimental Botany"},{"key":"2026091004222357100_erag323-B203","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s41588-018-0295-5","article-title":"A primer on deep learning in genomics","volume":"51","author":"Zou","year":"2019","journal-title":"Nature Genetics"},{"key":"2026091004222357100_erag323-B204","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-19921-4","article-title":"Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure","volume":"11","author":"Zrimec","year":"2020","journal-title":"Nature Communications"}],"container-title":["Journal of Experimental Botany"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jxb\/advance-article-pdf\/doi\/10.1093\/jxb\/erag323\/71017186\/erag323.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jxb\/advance-article-pdf\/doi\/10.1093\/jxb\/erag323\/71017186\/erag323.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:22:51Z","timestamp":1789028571000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jxb\/advance-article\/doi\/10.1093\/jxb\/erag323\/8789800"}},"subtitle":[],"editor":[{"given":"Zoran","family":"Nikoloski","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Potsdam ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":204,"URL":"https:\/\/doi.org\/10.1093\/jxb\/erag323","relation":{},"ISSN":["0022-0957","1460-2431"],"issn-type":[{"value":"0022-0957","type":"print"},{"value":"1460-2431","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]},"article-number":"erag323"}}