{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:44:42Z","timestamp":1785437082527,"version":"3.56.0"},"reference-count":57,"publisher":"Institute of Cytology and Genetics, SB RAS","issue":"8","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/vavilov.elpub.ru\/jour\/about\/editorialPolicies#openAccessPolicy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vestn. VOGiS"],"abstract":"<jats:p>The study of molecular genetic mechanisms of plant responses to specific growth conditions and stress factors is a central focus of scientific research aimed at developing new valuable crop varieties, particularly rice and wheat. These factors include abiotic stresses (high or low temperatures, drought, salinity, soil metal contamination), biotic stresses (pathogens, pests), as well as plant responses to regulatory factors (fertilizers, hormones, elicitors, and other compounds). Modern research in plant genetics is based on the understanding that the formation of any phenotypic characteristics (molecular genetic, biochemical, physiological, morphological, etc.) is controlled by gene networks \u2013 groups of coordinately functioning genes interacting through their products (RNA, proteins, and metabolites). Previously, we developed the ANDSystem intelligent technology designed to extract knowledge from scientific publication texts for the reconstruction of gene networks in biology and biomedicine. In this work, using an adapted version of ANDSystem for plants, we created the SmartCrop knowledge base designed to address challenges related to studying molecular genetic mechanisms of genotype-phenotype-environment interactions for agriculturally valuable rice and wheat crops. SmartCrop is designed to assist researchers in solving tasks such as interpreting omics technology results (establishing connections between gene sets and biological processes, phenotypic traits, etc.); reconstructing gene networks describing relationships between molecular genetic objects and concepts in breeding, phenomics, seed production, phytopathology, diagnostics, protective agents, etc.; identifying regulatory and signaling pathways of plant responses to specific growth conditions and biotic and abiotic stresses; predicting candidate genes for genotyping; searching for markers for marker-assisted selection; and identifying potential targets for substances (including external factors) affecting plants to ensure timely and uniform germination, better vegetative growth, efficient nutrient uptake, and improved stress resistance.\u00a0<\/jats:p>","DOI":"10.18699\/vjgb-25-129","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T08:51:50Z","timestamp":1769071910000},"page":"1221-1234","source":"Crossref","is-referenced-by-count":2,"title":["SmartCrop: knowledge base of molecular genetic mechanisms of rice and wheat adaptation to stress factors"],"prefix":"10.18699","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9433-8341","authenticated-orcid":true,"given":"P. S.","family":"Demenkov","sequence":"first","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-9155","authenticated-orcid":true,"given":"T. V.","family":"Ivanisenko","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7537-2525","authenticated-orcid":true,"given":"M. A.","family":"Kleshchev","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2158-3252","authenticated-orcid":true,"given":"E. A.","family":"Antropova","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9245-8988","authenticated-orcid":true,"given":"I. V.","family":"Yatsyk","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8472-4945","authenticated-orcid":true,"given":"A. R.","family":"Volyanskaya","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5923-3709","authenticated-orcid":true,"given":"A. V.","family":"Adamovskaya","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8510-7496","authenticated-orcid":true,"given":"A. V.","family":"Maltseva","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7419-5168","authenticated-orcid":true,"given":"A. S.","family":"Venzel","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5475-0443","authenticated-orcid":true,"given":"H.","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, College of Life Sciences, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-1699","authenticated-orcid":true,"given":"M.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, College of Life Sciences, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1859-4631","authenticated-orcid":true,"given":"V. A.","family":"Ivanisenko","sequence":"additional","affiliation":[{"name":"Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7950","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Alyahya N., Taybi T. Comparative transcriptomic profiling reveals dif ferentially expressed genes and important related metabolic path ways in shoots and roots of a Saudi wheat cultivar (Najran) under salinity stress. Front Plant Sci. 2023;14:1225541. doi 10.3389\/fpls.2023.1225541","DOI":"10.3389\/fpls.2023.1225541"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Antropova E.A., Volyanskaya A.R., Adamovskaya A.V., Demen kov P.S., Yatsyk I.V., Ivanisenko T.V., Orlov Y.L., Haoyu Ch., Chen M., Ivanisenko V.A. Computational identification of promis ing genetic markers associated with molecular mechanisms of re duced rice resistance to Rhizoctonia solani under excess nitrogen fertilization using gene network reconstruction and analysis methods. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Gene tics and Breeding. 2024;28(8):960-973. doi 10.18699\/vjgb-24-103","DOI":"10.18699\/vjgb-24-103"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Ayadi M., Brini F., Masmoudi K. Overexpression of a wheat aquapo rin gene, TdPIP2;1, enhances salt and drought tolerance in trans genic durum wheat cv. Maali. Int J Mol Sci. 2019;20(10):2389. doi 10.3390\/ijms20102389","DOI":"10.3390\/ijms20102389"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Chao H., Zhang S., Hu Y., Ni Q., Xin S., Zhao L., Ivanisenko V.A., Orlov Y.L., Chen M. Integrating omics databases for enhanced crop breeding. J Integr Bioinform. 2023;20(4):20230012. doi 10.1515\/jib-2023-0012","DOI":"10.1515\/jib-2023-0012"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Chen T., Nomura K., Wang X., Sohrabi R., Xu J., Yao L., Paasch B.C., Ma L., Kremer J., Cheng Y., Zhang L., Wang N., Wang E., Xin X.F., He S.Y. A plant genetic network for preventing dysbiosis in the phyl losphere. Nature. 2020;580(7805):653-657. doi 10.1038\/s41586020-2185-0","DOI":"10.1038\/s41586-020-2185-0"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Cheng M., Zhu Y., Yu H., Shao L., Zhang Y., Li L., Tu H., \u2026 Orlov Y.L., Chen D., Wong A., Yang Y.E., Chen M. Non-coding RNA nota tions, regulations and interactive resources. Funct Integr Genomics. 2024;24(6):217. doi 10.1007\/s10142-024-01494-w","DOI":"10.1007\/s10142-024-01494-w"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Colebrook E.H., Thomas S.G., Phillips A.L., Hedden P. The role of gib berellin signalling in plant responses to abiotic stress. J Exp Biol. 2014;217(1):67-75. doi 10.1242\/jeb.089938","DOI":"10.1242\/jeb.089938"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Demenkov P.S., Ivanisenko T.V., Kolchanov N.A., Ivanisenko V.A. ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem. In Silico Biol. 2012;11(3-4):149-161. doi 10.3233\/isb-2012-0449","DOI":"10.3233\/ISB-2012-0449"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Demenkov P.S., Saik O.V., Ivanisenko T.V., Kolchanov N.A., Koche tov A.V., Ivanisenko V.A. Prioritization of potato genes involved in the formation of agronomically valuable traits using the SOLA NUM TUBEROSUM knowledge base. Vavilovskii Zhurnal Gene tiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2019; 23(3):312-319. doi 10.18699\/VJ19.501","DOI":"10.18699\/VJ19.501"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Demenkov P.S., Oshchepkova E.A., Ivanisenko T.V., Ivanisenko V.A. Prioritization of biological processes based on the reconstruction and analysis of associative gene networks describing the response of plants to adverse environmental factors. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2021;25(5):580-592. doi 10.18699\/VJ21.065","DOI":"10.18699\/VJ21.065"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Do H., Than K., Larmande P. Evaluating named-entity recognition ap proaches in plant molecular biology. In: Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science. Vol. 11248. Springer, Cham., 2018;219-225. doi 10.1007\/978-3-030-03014-8_19","DOI":"10.1007\/978-3-030-03014-8_19"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"D\u2019Souza J. Agriculture named entity recognition \u2013 towards FAIR, reusable scholarly contributions in agriculture. Knowledge. 2024; 4(1):1-26. doi 10.3390\/knowledge4010001","DOI":"10.3390\/knowledge4010001"},{"key":"ref13","unstructured":"Gemma Team, Google DeepMind. Gemma 2: improving open language models at a practical size. arXiv. 2024:2408.00118. doi 10.48550\/arXiv.2408.00118"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Guindani L.G., Oliveirai G.A., Ribeiro M.H.D.M., Gonzalez G.V., de Lima J.D. Exploring current trends in agricultural commodities fore casting methods through text mining: developments in statistical and artificial intelligence methods. Heliyon. 2024;10(23):e40568. doi 10.1016\/j.heliyon.2024.e40568","DOI":"10.1016\/j.heliyon.2024.e40568"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Gupta A., Shaw B.P., Sahu B.B. Post-translational regulation of the membrane transporters contributing to salt tolerance in plants. Funct Plant Biol. 2021;48(12):1199-1212. doi 10.1071\/FP21153","DOI":"10.1071\/FP21153"},{"key":"ref16","unstructured":"Hamilton W.L., Ying R., Leskovec J. Inductive representation learning on large graphs. arXiv. 2017. doi 10.48550\/arxiv.1706.02216"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Hoai P.T.T., Tyerman S.D., Schnell N., Tucker M., McGaughey S.A., Qiu J., Groszmann M., Byrt C.S. Deciphering aquaporin regulation and roles in seed biology. J Exp Bot. 2020;71(6):1763-1773. doi 10.1093\/jxb\/erz555","DOI":"10.1093\/jxb\/erz555"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Islamaj R., Wei C.H., Cissel D., Miliaras N., Printseva O., Rodio nov O., Sekiya K., Ward J., Lu Z. NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition. J Biomed Inform. 2021;118:103779. doi 10.1016\/j.jbi.2021.103779","DOI":"10.1016\/j.jbi.2021.103779"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Ivanisenko T.V., Demenkov P.S., Ivanisenko V.A. Text mining on PubMed. In: Chen M., Hofest\u00e4dt R. (Eds) Approaches in Integrative Bioinformatics. Springer, 2014;161-170. doi 10.1007\/978-3-64241281-3_6","DOI":"10.1007\/978-3-642-41281-3_6"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Ivanisenko T.V., Saik O.V., Demenkov P.S., Khlestkin V.K., Khlest kina E.K., Kolchanov N.A., Ivanisenko V.A. The SOLANUM TUBEROSUM knowledge base: the section on molecular-genetic regulation of metabolic pathways. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2018;22(1): 8-17. doi 10.18699\/VJ18.325 (in Russian)","DOI":"10.18699\/VJ18.325"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Ivanisenko T.V., Saik O.V., Demenkov P.S., Ivanisenko N.V., Savostia nov A.N., Ivanisenko V.A. ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature. BMC Bioinformatics. 2020;21(S11):228. doi 10.1186\/s12859-02003557-8","DOI":"10.1186\/s12859-020-03557-8"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Ivanisenko T.V., Demenkov P.S., Kolchanov N.A., Ivanisenko V.A. The new version of the ANDDigest tool with improved AI-based short names recognition. Int J Mol Sci. 2022;23(23):14934. doi 10.3390\/ijms232314934","DOI":"10.3390\/ijms232314934"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Ivanisenko T.V., Demenkov P.S., Ivanisenko V.A. An accurate and effi cient approach to knowledge extraction from scientific publications using structured ontology models, graph neural networks, and large language models. Int J Mol Sci. 2024;25(21):11811. doi 10.3390\/ijms252111811","DOI":"10.3390\/ijms252111811"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Ivanisenko V.A., Saik O.V., Ivanisenko N.V., Tiys E.S., Ivanisenko T.V., Demenkov P.S., Kolchanov N.A. ANDSystem: an Associative Net work Discovery System for automated literature mining in the field of biology. BMC Syst Biol. 2015;9(Suppl.2):S2. doi 10.1186\/17520509-9-s2-s2","DOI":"10.1186\/1752-0509-9-S2-S2"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Ivanisenko V.A., Demenkov P.S., Ivanisenko T.V., Mishchenko E.L., Saik O.V. A new version of the ANDSystem tool for automatic ex traction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019;20(S1):34. doi 10.1186\/s12859-018-2567-6","DOI":"10.1186\/s12859-018-2567-6"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Kanehisa M. Molecular network analysis of diseases and drugs in KEGG. In: Mamitsuka H., DeLisi C., Kanehisa M. (Eds) Data Mining for Systems Biology. Methods in Molecular Biology. Vol. 939. Humana Press, 2013;263-275. doi 10.1007\/978-1-62703-107-3_17","DOI":"10.1007\/978-1-62703-107-3_17"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Kleshchev M.A., Maltseva A.V., Antropova E.A., Demenkov P.S., Ivanisenko T.V., Orlov Y.L., Chao H., Chen M., Kolchanov N.A., Ivanisenko V.A. Reconstruction and computational analysis of the microRNA regulation gene network in wheat drought response mechanisms. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Jour nal of Genetics and Breeding. 2024;28(8):904-917. doi 10.18699\/vjgb-24-98","DOI":"10.18699\/vjgb-24-98"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Kong W., Sun T., Zhang C., Deng X., Li Y. Comparative tran scriptome analysis reveals the mechanisms underlying dif ferences in salt to lerance between indica and japonica rice at seedling stage. Front Plant Sci. 2021;12:725436. doi 10.3389\/fpls.2021.725436","DOI":"10.3389\/fpls.2021.725436"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Krallinger M., Rabal O., Leitner F., Vazquez M., Salgado D., Lu Z., Leaman R., \u2026 Alves R., Segura-Bedmar I., Mart\u00ednez P., Oyarza bal J., Valencia A. The CHEMDNER corpus of chemicals and drugs and its annotation principles. J Cheminform. 2015;7(S1):S2. doi 10.1186\/1758-2946-7-s1-s2","DOI":"10.1186\/1758-2946-7-S1-S1"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Lei L., Cao L., Ding G., Zhou J., Luo Y., Bai L., Xia T., \u2026 Xie T., Yang G., Wang X., Sun S., Lai Y. OsBBX11 on qSTS4 links to salt tolerance at the seeding stage in Oryza sativa L. ssp. Japonica. Front Plant Sci. 2023;14:1139961. doi 10.3389\/fpls.2023.1139961","DOI":"10.3389\/fpls.2023.1139961"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Lesk C., Rowhani P., Ramankutty N. Influence of extreme weather di sasters on global crop production. Nature. 2016;529(7584):84-87. doi 10.1038\/nature16467","DOI":"10.1038\/nature16467"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Liu L., Liu E., Hu Y., Li S., Zhang S., Chao H., Hu Y., Zhu Y., Chen Y., Xie L., Shen Y., Wu L., Chen M. ncPlantDB: a plant ncRNA data base with potential ncPEP information and cell type-specific interac tion. Nucleic Acids Res. 2025;53(D1):D1587-D1594. doi 10.1093\/nar\/gkae1017","DOI":"10.1093\/nar\/gkae1017"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Mittler R. Abiotic stress, the field environment and stress combina tion. Trends Plant Sci. 2006;11(1):15-19. doi 10.1016\/j.tplants.2005.11.002","DOI":"10.1016\/j.tplants.2005.11.002"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Naithani S., Gupta P., Preece J., D\u2019Eustachio P., Elser J.L., Garg P., Dikeman D.A., \u2026 Bolton E., Papatheodorou I., Stein L., Ware D., Jaiswal P. Plant Reactome: a knowledgebase and resource for com parative pathway analysis. Nucleic Acids Res. 2020;48(D1):D1093 D1103. doi 10.1093\/nar\/gkz996","DOI":"10.1093\/nar\/gkz996"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Ni Y., Aghamirzaie D., Elmarakeby H., Collakova E., Li S., Grene R., Heath L.S. A machine learning approach to predict gene regulato ry networks in seed development in Arabidopsis. Front Plant Sci. 2016;7:1936. doi 10.3389\/fpls.2016.01936","DOI":"10.3389\/fpls.2016.01936"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Nykiel M., Gietler M., Fidler J., Prabucka B., Labudda M. Abiotic stress signaling and responses in plants. Plants. 2023;12(19):3405. doi 10.3390\/plants12193405","DOI":"10.3390\/plants12193405"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Otasek D., Morris J.H., Bou\u00e7as J., Pico A.R., Demchak B. Cyto scape Automation: empowering workflow-based network analysis. Genome Biol. 2019;20(1):185. doi 10.1186\/s13059-019-1758-4","DOI":"10.1186\/s13059-019-1758-4"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Pearson H. Biology\u2019s name game. Nature. 2001;411(6838):631-632. doi 10.1038\/35079694","DOI":"10.1038\/35079694"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Saha C., Saha S., Bhattacharyya N.P. LncRNAOmics: a comprehensive review of long non-coding RNAs in plants. Genes. 2025;16(7):765. doi 10.3390\/genes16070765","DOI":"10.3390\/genes16070765"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Saik O.V., Demenkov P.S., Ivanisenko T.V., Kolchanov N.A., Ivan isenko V.A. Development of methods for automatic extraction of knowledge from texts of scientific publications for the crea tion of a knowledge base SOLANUM TUBEROSUM. Agric Biol. 2017;52(1):63-74. doi 10.15389\/agrobiology.2017.1.63eng","DOI":"10.15389\/agrobiology.2017.1.63eng"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Shewry P.R., Hey S.J. The contribution of wheat to human diet and health. Food Energy Secur. 2015;4(3):178-202. doi 10.1002\/fes3.64","DOI":"10.1002\/fes3.64"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Shrestha A.M.S., Gonzales M.E.M., Ong P.C.L., Larmande P., Lee H.S., Jeung J.U., Kohli A., Chebotarov D., Mauleon R.P., Lee J.S., McNally K.L. RicePilaf: a post-GWAS\/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, path way, and text-mining information to provide functional insights into rice QTLs and GWAS loci. GigaScience. 2024;13:giae013. doi 10.1093\/gigascience\/giae013","DOI":"10.1093\/gigascience\/giae013"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Statello L., Guo C.J., Chen L.L., Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22:96-118. doi 10.1038\/s41580-020-00315-9","DOI":"10.1038\/s41580-020-00315-9"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Sun X., Zheng H., Sui N. Regulation mechanism of long non-coding RNA in plant response to stress. Biochem Biophys Res Commun. 2018;503(2):402-407. doi 10.1016\/j.bbrc.2018.07.072","DOI":"10.1016\/j.bbrc.2018.07.072"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Supriya P., Srividya G.K., Solanki M., Manvitha D., Prakasam V., Balakrishnan M., Neeraja C.N., Rao C.S., Sundaram R.M., Man grauthia S.K. Identification and expression analysis of long non coding RNAs of rice induced during interaction with Rhizoctonia solani. Physiol Mol Plant Pathol. 2024;134:102389. doi 10.1016\/j.pmpp.2024.102389","DOI":"10.1016\/j.pmpp.2024.102389"},{"key":"ref46","doi-asserted-by":"crossref","unstructured":"Szklarczyk D., Gable A.L., Nastou K.C., Lyon D., Kirsch R., Pyysa lo S., Doncheva N.T., Legeay M., Fang T., Bork P., Jensen L.J., von Mering C. The STRING database in 2021: customizable protein protein networks, and functional characterization of user-uploaded gene\/measurement sets. Nucleic Acids Res. 2021;49(D1):D605 D612. doi 10.1093\/nar\/gkaa1074","DOI":"10.1093\/nar\/gkaa1074"},{"key":"ref47","doi-asserted-by":"crossref","unstructured":"Tian F., Yang D.C., Meng Y.Q., Jin J., Gao G. PlantRegMap: chart ing functional regulatory maps in plants. Nucleic Acids Res. 2020; 48(D1):D1104-D1113. doi 10.1093\/nar\/gkz1020","DOI":"10.1093\/nar\/gkz1020"},{"key":"ref48","unstructured":"Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser L., Polosukhin I. Attention is all you need. arXiv. 2017. doi 10.48550\/arXiv.1706.03762"},{"key":"ref49","doi-asserted-by":"crossref","unstructured":"Verma V., Ravindran P., Kumar P.P. Plant hormone-mediated regulation of stress responses. BMC Plant Biol. 2016;16(1):86. doi 10.1186\/s12870-016-0771-y Virlouvet","DOI":"10.1186\/s12870-016-0771-y"},{"key":"ref50","doi-asserted-by":"crossref","unstructured":"L., Avenson T.J., Du Q., Zhang C., Liu N., Fromm M., Avramova Z., Russo S.E. Dehydration stress memory: gene net works linked to physiological responses during repeated stresses of Zea mays. Front Plant Sci. 2018;9:1058. doi 10.3389\/fpls.2018.01058","DOI":"10.3389\/fpls.2018.01058"},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"Volyanskaya A.R., Antropova E.A., Zubairova U.S., Demenkov P.S., Venzel A.S., Orlov Y.L., Makarova A.A., Ivanisenko T.V., Gorsh kova T.A., Aglyamova A.R., Kolchanov N.A., Chen M., Ivanisen ko V.A. Reconstruction and analysis of the gene regulatory network for cell wall function in Arabidopsis thaliana L. leaves in response to water deficit. Vavilov J Genet Breed. 2023;27(8):1031-1041. doi 10.18699\/vjgb-23-118","DOI":"10.18699\/VJGB-23-118"},{"key":"ref52","doi-asserted-by":"crossref","unstructured":"Wang S., Shi M., Zhang Y., Xie X., Sun P., Fang C., Zhao J. FvMYB24, a strawberry R2R3-MYB transcription factor, improved salt stress tolerance in transgenic Arabidopsis. Biochem Biophysical Res Com mun. 2021;569:93-99. doi 10.1016\/j.bbrc.2021.06.085","DOI":"10.1016\/j.bbrc.2021.06.085"},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"Wang X., Niu Y., Zheng Y. Multiple functions of MYB transcription factors in abiotic stress responses. Int J Mol Sci. 2021;22(11):6125. doi 10.3390\/ijms22116125","DOI":"10.3390\/ijms22116125"},{"key":"ref54","doi-asserted-by":"crossref","unstructured":"Wei T., Guo D., Liu J. PtrMYB3, a R2R3-MYB transcription factor from Poncirus trifoliata, negatively regulates salt tolerance and hydrogen peroxide scavenging. Antioxidants. 2021;10(9):1388. doi 10.3390\/antiox10091388","DOI":"10.3390\/antiox10091388"},{"key":"ref55","doi-asserted-by":"crossref","unstructured":"Yan J., Wang X. Unsupervised and semi\u2010supervised learning: the next frontier in machine learning for plant systems biology. Plant J. 2022;111(6):1527-1538. doi 10.1111\/tpj.15905","DOI":"10.1111\/tpj.15905"},{"key":"ref56","doi-asserted-by":"crossref","unstructured":"Zhang D., Zhao R., Xian G., Kou Y., Ma W. A new model construc tion based on the knowledge graph for mining elite polyphenotype genes in crops. Front Plant Sci. 2024;15:1361716. doi 10.3389\/fpls.2024.1361716","DOI":"10.3389\/fpls.2024.1361716"},{"key":"ref57","doi-asserted-by":"crossref","unstructured":"Zhao S., Zhang Q., Liu M., Zhou H., Ma C., Wang P. Regulation of plant responses to salt stress. Int J Mol Sci. 2021;22(9):4609. doi 10.3390\/ijms22094609","DOI":"10.3390\/ijms22094609"}],"container-title":["Vavilov Journal of Genetics and Breeding"],"original-title":[],"link":[{"URL":"https:\/\/vavilov.elpub.ru\/jour\/article\/viewFile\/4922\/2037","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T09:22:33Z","timestamp":1769073753000},"score":1,"resource":{"primary":{"URL":"https:\/\/vavilov.elpub.ru\/jour\/article\/view\/4922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,11]]},"references-count":57,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,1,11]]}},"URL":"https:\/\/doi.org\/10.18699\/vjgb-25-129","relation":{},"ISSN":["2500-3259"],"issn-type":[{"value":"2500-3259","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,11]]}}}