{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T17:04:48Z","timestamp":1794330288160,"version":"4.9.0"},"reference-count":78,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["bmn.com","cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Trends in Plant Science"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.tplants.2024.04.013","type":"journal-article","created":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T04:10:41Z","timestamp":1716696641000},"page":"1145-1155","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":66,"title":["Large language models in plant biology"],"prefix":"10.1016","volume":"29","author":[{"given":"Hilbert Yuen In","family":"Lam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing Er","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7848-0126","authenticated-orcid":false,"given":"Marek","family":"Mutwil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.tplants.2024.04.013_bb0005","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0010","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1111\/nph.18771","article-title":"One hundred important questions facing plant science: an international perspective","volume":"238","author":"Armstrong","year":"2023","journal-title":"New Phytol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0015","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.tplants.2023.06.008","article-title":"One hundred important questions facing plant science derived using a large language model","volume":"29","author":"Agathokleous","year":"2023","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.tplants.2024.04.013_bb0020","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":"10.1016\/j.tplants.2024.04.013_bb0025","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.tplants.2024.04.013_bb0030","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","article-title":"ProteinBERT: a universal deep-learning model of protein sequence and function","volume":"38","author":"Brandes","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.tplants.2024.04.013_bb0035","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1093\/bioinformatics\/btab083","article-title":"DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome","volume":"37","author":"Ji","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.tplants.2024.04.013_bb0040","article-title":"To transformers and beyond: large language models for the genome","author":"Consens","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0045","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1038\/s41598-020-80786-0","article-title":"Embeddings from deep learning transfer GO annotations beyond homology","volume":"11","author":"Littmann","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.tplants.2024.04.013_bb0050","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1007\/s00439-021-02411-y","article-title":"Embeddings from protein language models predict conservation and variant effects","volume":"141","author":"Marquet","year":"2022","journal-title":"Hum. Genet."},{"key":"10.1016\/j.tplants.2024.04.013_bb0055","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1038\/s41467-022-32007-7","article-title":"ProtGPT2 is a deep unsupervised language model for protein design","volume":"13","author":"Ferruz","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.tplants.2024.04.013_bb0060","article-title":"GenSLMs: genome-scale language models reveal SARS-CoV-2 evolutionary dynamics","author":"Zvyagin","year":"2022","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0065","article-title":"Codon language embeddings provide strong signals for protein engineering","author":"Outeiral","year":"2022","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0070","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1002\/fee.2320","article-title":"Trends in ecology and conservation over eight decades","volume":"19","author":"Anderson","year":"2021","journal-title":"Front. Ecol. Environ."},{"key":"10.1016\/j.tplants.2024.04.013_bb0075","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s12870-019-1693-2","article-title":"A k-mer grammar analysis to uncover maize regulatory architecture","volume":"19","author":"Mej\u00eda-Guerra","year":"2019","journal-title":"BMC Plant Biol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0080","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2022.807522","article-title":"DWPPI: a deep learning approach for predicting protein\u2013protein interactions in plants based on multi-source information with a large-scale biological network","volume":"10","author":"Pan","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0085","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s12539-022-00503-5","article-title":"RicENN: prediction of rice enhancers with neural network based on DNA sequences","volume":"14","author":"Gao","year":"2022","journal-title":"Interdiscip. Sci.: Comput. Life Sci."},{"key":"10.1016\/j.tplants.2024.04.013_bb0090","article-title":"Incorporating deep learning with word embedding to identify plant ubiquitylation sites","volume":"8","author":"Wang","year":"2020","journal-title":"Front. Cell Dev. Biol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0095","doi-asserted-by":"crossref","first-page":"6141","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":"Nat. Commun."},{"key":"10.1016\/j.tplants.2024.04.013_bb0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.biopha.2023.115077","article-title":"Deep learning applications in single-cell genomics and transcriptomics data analysis","volume":"165","author":"Erfanian","year":"2023","journal-title":"Biomed. Pharmacother."},{"key":"10.1016\/j.tplants.2024.04.013_bb0105","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":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.tplants.2024.04.013_bb0110","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":"Nat. Biotechnol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0115","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s13007-018-0333-4","article-title":"Deep phenotyping: deep learning for temporal phenotype\/genotype classification","volume":"14","author":"Taghavi Namin","year":"2018","journal-title":"Plant Methods"},{"key":"10.1016\/j.tplants.2024.04.013_bb0120","article-title":"On the difficulty of training recurrent neural networks","author":"Pascanu","year":"2013","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0125","series-title":"Advances in Neural Information Processing Systems 30 (NIPS 2017)","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.tplants.2024.04.013_bb0130","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":"Nat. Methods"},{"key":"10.1016\/j.tplants.2024.04.013_bb0135","article-title":"DNABERT-2: efficient foundation model and benchmark for multi-species genome","author":"Zhou","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0140","article-title":"scGPT: towards building a foundation model for single-cell multi-omics using generative AI","author":"Cui","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0145","article-title":"Multi-head or single-head? An empirical comparison for transformer training","author":"Liu","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0150","article-title":"HyenaDNA: long-range genomic sequence modeling at single nucleotide resolution","author":"Nguyen","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0155","author":"OpenAI"},{"key":"10.1016\/j.tplants.2024.04.013_bb0160","article-title":"PlantConnectome: knowledge networks encompassing >100,000 plant article abstracts","author":"Fo","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0165","article-title":"Don\u2019t stop pretraining: adapt language models to domains and tasks","author":"Gururangan","year":"2020","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0170","article-title":"The nucleotide transformer: building and evaluating robust foundation models for human genomics","author":"Dalla-Torre","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0175","article-title":"RoBERTa: a robustly optimized BERT pretraining approach","author":"Liu","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0180","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"10.1016\/j.tplants.2024.04.013_bb0185","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1038\/s41467-022-28865-w","article-title":"Improved prediction of protein-protein interactions using AlphaFold2","volume":"13","author":"Bryant","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.tplants.2024.04.013_bb0190","doi-asserted-by":"crossref","DOI":"10.3389\/fbinf.2023.1120370","article-title":"Before and after AlphaFold2: an overview of protein structure prediction","volume":"3","author":"Bertoline","year":"2023","journal-title":"Front. Bioinform."},{"key":"10.1016\/j.tplants.2024.04.013_bb0195","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.tplants.2024.04.013_bb0200","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2311219120","article-title":"DNA language models are powerful predictors of genome-wide variant effects","volume":"120","author":"Benegas","year":"2023","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.tplants.2024.04.013_bb0205","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1101\/gr.097857.109","article-title":"Detection of nonneutral substitution rates on mammalian phylogenies","volume":"20","author":"Pollard","year":"2010","journal-title":"Genome Res."},{"key":"10.1016\/j.tplants.2024.04.013_bb0210","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1101\/gr.3715005","article-title":"Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes","volume":"15","author":"Siepel","year":"2005","journal-title":"Genome Res."},{"key":"10.1016\/j.tplants.2024.04.013_bb0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101805","article-title":"Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence","volume":"99","author":"Ali","year":"2023","journal-title":"Inform. Fusion"},{"key":"10.1016\/j.tplants.2024.04.013_bb0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108391","article-title":"Interpretability in the medical field: a systematic mapping and review study","volume":"117","author":"Hakkoum","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.tplants.2024.04.013_bb0225","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1016\/j.tplants.2022.08.010","article-title":"Toward learning the principles of plant gene regulation","volume":"27","author":"Zrimec","year":"2022","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.tplants.2024.04.013_bb0230","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.pbi.2019.12.010","article-title":"Deep learning for plant genomics and crop improvement","volume":"54","author":"Wang","year":"2020","journal-title":"Curr. Opin. Plant Biol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0235","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0240","article-title":"Learning important features through propagating activation differences","author":"Shrikumar","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0245","article-title":"Techniques for interpretable machine learning","author":"Du","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0250","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s42803-023-00075-w","article-title":"On reading and interpreting black box deep neural networks","volume":"5","author":"Dobson","year":"2023","journal-title":"Int. J. Digit. Humanit."},{"key":"10.1016\/j.tplants.2024.04.013_bb0255","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1038\/s42256-023-00620-w","article-title":"Evaluation of post-hoc interpretability methods in time-series classification","volume":"5","author":"Turb\u00e9","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.tplants.2024.04.013_bb0260","series-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations","first-page":"37","article-title":"A multiscale visualization of attention in the transformer model","author":"Vig","year":"2019"},{"key":"10.1016\/j.tplants.2024.04.013_bb0265","doi-asserted-by":"crossref","first-page":"3488","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":"Nat. Commun."},{"key":"10.1016\/j.tplants.2024.04.013_bb0270","article-title":"FloraBERT: cross-species transfer learning with attention-based neural networks for gene expression prediction","author":"Levy","year":"2022","journal-title":"Res. Square"},{"key":"10.1016\/j.tplants.2024.04.013_bb0275","article-title":"A foundational large language model for edible plant genomes","author":"Mendoza-Revilla","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0280","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.tplants.2021.10.006","article-title":"Twenty years of plant genome sequencing: achievements and challenges","volume":"27","author":"Sun","year":"2022","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.tplants.2024.04.013_bb0285","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1016\/j.cell.2021.04.014","article-title":"A cis-regulatory atlas in maize at single-cell resolution","volume":"184","author":"Marand","year":"2021","journal-title":"Cell"},{"key":"10.1016\/j.tplants.2024.04.013_bb0290","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1093\/plphys\/kiab429","article-title":"To the proteome and beyond: advances in single-cell omics profiling for plant systems","volume":"188","author":"Clark","year":"2022","journal-title":"Plant Physiol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0295","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1038\/s41589-023-01327-0","article-title":"Single-cell multi-omics in the medicinal plant Catharanthus roseus","volume":"19","author":"Li","year":"2023","journal-title":"Nat. Chem. Biol."},{"key":"10.1016\/j.tplants.2024.04.013_bb0300","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.27041","article-title":"The Human Cell Atlas","volume":"6","author":"Regev","year":"2017","journal-title":"eLife"},{"key":"10.1016\/j.tplants.2024.04.013_bb0305","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.tplants.2019.01.006","article-title":"Towards building a plant cell atlas","volume":"24","author":"Rhee","year":"2019","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.tplants.2024.04.013_bb0310","article-title":"A single-nucleus atlas of seed-to-seed development in Arabidopsis","author":"Lee","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0315","doi-asserted-by":"crossref","DOI":"10.1126\/science.adf4721","article-title":"Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root","volume":"379","author":"Nolan","year":"2023","journal-title":"Science"},{"key":"10.1016\/j.tplants.2024.04.013_bb0320","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.devcel.2022.01.008","article-title":"A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants","volume":"57","author":"Shahan","year":"2022","journal-title":"Dev. Cell"},{"key":"10.1016\/j.tplants.2024.04.013_bb0325","article-title":"Time-resolved single-cell and spatial gene regulatory atlas of plants under pathogen attack","author":"Nobori","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2023.112676","article-title":"Single-cell profiling of Arabidopsis leaves to Pseudomonas syringae infection","volume":"42","author":"Zhu","year":"2023","journal-title":"Cell Rep."},{"key":"10.1016\/j.tplants.2024.04.013_bb0335","doi-asserted-by":"crossref","first-page":"3261","DOI":"10.1093\/plcell\/koac167","article-title":"Leaf cell-specific and single-cell transcriptional profiling reveals a role for the palisade layer in UV light protection","volume":"34","author":"Procko","year":"2022","journal-title":"Plant Cell"},{"key":"10.1016\/j.tplants.2024.04.013_bb0340","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1038\/s41467-022-29268-7","article-title":"Current progress and open challenges for applying deep learning across the biosciences","volume":"13","author":"Sapoval","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.tplants.2024.04.013_bb0345","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.tplants.2024.04.013_bb0350","article-title":"Scaling laws for neural language models","author":"Kaplan","year":"2020","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0355","article-title":"Challenges and applications of large language models","author":"Kaddour","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0360","author":"Meta"},{"key":"10.1016\/j.tplants.2024.04.013_bb0365","article-title":"FlashAttention: fast and memory-efficient exact attention with IO-awareness","author":"Dao","year":"2022","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0370","article-title":"GENA-LM: a family of open-source foundational DNA language models for long sequences","author":"Fishman","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0375","article-title":"LoRA: low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0380","article-title":"Large language models in molecular biology","author":"Batzoglou","year":"2023","journal-title":"Medium"},{"key":"10.1016\/j.tplants.2024.04.013_bb0385","article-title":"Mamba: linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.tplants.2024.04.013_bb0390","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."}],"container-title":["Trends in Plant Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1360138524001183?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1360138524001183?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T15:49:22Z","timestamp":1727884162000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1360138524001183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":78,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["S1360138524001183"],"URL":"https:\/\/doi.org\/10.1016\/j.tplants.2024.04.013","relation":{},"ISSN":["1360-1385"],"issn-type":[{"value":"1360-1385","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Large language models in plant biology","name":"articletitle","label":"Article Title"},{"value":"Trends in Plant Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.tplants.2024.04.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}