{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T22:55:08Z","timestamp":1772578508718,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Theory Biosci."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s12064-026-00460-3","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T09:12:55Z","timestamp":1772529175000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CAGAD: dynamic community attention for prediction gene regulatory network"],"prefix":"10.1007","volume":"145","author":[{"given":"Sura I. Mohammed","family":"Ali","sequence":"first","affiliation":[]},{"given":"Sura Zaki","family":"AlRashid","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"issue":"1","key":"460_CR1","doi-asserted-by":"publisher","first-page":"703","DOI":"10.11591\/ijai.v13.i1.pp703-710","volume":"13","author":"SIM Ali","year":"2024","unstructured":"Ali SIM, Nihad M, Sharaf HM, Farouk H (2024) Machine learning for text document classification-efficient classification approach. IAES Int J Artif Intell 13(1):703\u2013710. https:\/\/doi.org\/10.11591\/ijai.v13.i1.pp703-710","journal-title":"IAES Int J Artif Intell"},{"key":"460_CR2","doi-asserted-by":"publisher","unstructured":"Al-Mashanji AK, Al-Rashi SZ (2019) Computational methods for preprocessing and classifying gene expression data-survey. In: 4th Scientific International Conference Najaf (SICN). https:\/\/doi.org\/10.1109\/SICN47020.2019.9019349.","DOI":"10.1109\/SICN47020.2019.9019349"},{"issue":"9","key":"460_CR3","first-page":"3422","volume":"8","author":"SZ Al-Mshanji","year":"2019","unstructured":"Al-Mshanji SZ, Ameer A, Al-Rashid (2019) Improving clustering algorithm for gene expression data using hybrid algorithm. Compusoft; Mumbai 8(9):3422\u20133430","journal-title":"Compusoft; Mumbai"},{"issue":"2","key":"460_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJeC.304035","volume":"18","author":"SZ AlRashid","year":"2022","unstructured":"AlRashid SZ, Dosh MH, Obaid AJ (2022) Classification of the senescence-accelerated mouse (SAM) strains with its behaviour using deep learning. Int J e-Collab 18(2):1\u201313. https:\/\/doi.org\/10.4018\/IJeC.304035","journal-title":"Int J e-Collab"},{"issue":"1","key":"460_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-022-04186-y","volume":"5","author":"T Cui","year":"2022","unstructured":"Cui T, El Mekkaoui K, Reinvall J, Havulinna AS, Marttinen P, Kaski S (2022) Gene\u2013gene interaction detection with deep learning. Commun Biol 5(1):1\u201312. https:\/\/doi.org\/10.1038\/s42003-022-04186-y","journal-title":"Commun Biol"},{"issue":"3","key":"460_CR6","first-page":"1325","volume":"9","author":"SA Fattah","year":"2020","unstructured":"Fattah SA, Lafta HA (2020) Alrashid SZ (2020) B-pred: An intelligent and adaptable medical diagnosis system based on bagging machine learning. Int J Sci Technol Res 9(3):1325\u20131331","journal-title":"Int J Sci Technol Res"},{"issue":"4","key":"460_CR7","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1002\/qub2.26","volume":"11","author":"K Feng","year":"2023","unstructured":"Feng K, Jiang H, Yin C, Sun H (2023) Gene regulatory network inference based on causal discovery integrating with graph neural network. Quant Biol 11(4):434\u2013450. https:\/\/doi.org\/10.1002\/qub2.26","journal-title":"Quant Biol"},{"key":"460_CR8","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae143","author":"Z Gao","year":"2024","unstructured":"Gao Z et al (2024) DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding. Brief Bioinf. https:\/\/doi.org\/10.1093\/bib\/bbae143","journal-title":"Brief Bioinf"},{"issue":"1","key":"460_CR9","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s10479-011-0841-3","volume":"219","author":"CR Garc\u00eda-Alonso","year":"2014","unstructured":"Garc\u00eda-Alonso CR, P\u00e9rez-Naranjo LM, Fern\u00e1ndez-Caballero JC (2014) Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms. Ann Oper Res 219(1):187\u2013202. https:\/\/doi.org\/10.1007\/s10479-011-0841-3","journal-title":"Ann Oper Res"},{"key":"460_CR10","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf462","author":"Y Gong","year":"2025","unstructured":"Gong Y (2025) DeepPhosPPI\u202f: a deep learning framework with attention-CNN and transformer for predicting phosphorylation effects on protein \u2013 protein interactions. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbaf462","journal-title":"Brief Bioinform"},{"key":"460_CR11","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf396","author":"G Gu","year":"2025","unstructured":"Gu G et al (2025) MVSGDR: multi-view stacked graph convolutional network for drug repositioning. Brief Bioinf. https:\/\/doi.org\/10.1093\/bib\/bbaf396","journal-title":"Brief Bioinf"},{"key":"460_CR12","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf470","author":"A Hegde","year":"2025","unstructured":"Hegde A, Nguyen T, Cheng J (2025) Machine learning methods for gene regulatory network inference. Brief Bioinf. https:\/\/doi.org\/10.1093\/bib\/bbaf470","journal-title":"Brief Bioinf"},{"key":"460_CR13","doi-asserted-by":"crossref","unstructured":"Ibrahim S, Ali M, Zaki S, Rashid A (2025) Integrated features based on graph clustering and gene expression. In: ProceedingsProceedings of the 13th International Conference Conference on Appliedon Applied Innovations in IT (ICAIIT) pp. 253\u2013262","DOI":"10.22541\/au.173833148.87987684\/v1"},{"issue":"Suppl 2","key":"460_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12918-019-0694-y","volume":"13","author":"K Kc","year":"2019","unstructured":"Kc K, Li R, Cui F, Yu Q, Haake AR (2019) GNE: a deep learning framework for gene network inference by aggregating biological information. BMC Syst Biol 13(Suppl 2):1\u201314. https:\/\/doi.org\/10.1186\/s12918-019-0694-y","journal-title":"BMC Syst Biol"},{"issue":"3","key":"460_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbad129","volume":"24","author":"W Liu","year":"2023","unstructured":"Liu W et al (2023) NSRGRN: a network structure refinement method for gene regulatory network inference. Brief Bioinf 24(3):1\u201312. https:\/\/doi.org\/10.1093\/bib\/bbad129","journal-title":"Brief Bioinf"},{"issue":"6","key":"460_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbad414","volume":"24","author":"G Mao","year":"2023","unstructured":"Mao G et al (2023) Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. Brief Bioinf 24(6):1\u201311. https:\/\/doi.org\/10.1093\/bib\/bbad414","journal-title":"Brief Bioinf"},{"key":"460_CR17","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1038\/nmeth.2016.Wisdom","volume":"9","author":"D Marbach","year":"2016","unstructured":"Marbach D et al (2016) Wisdom of crowds for robust gene network inference the DREAM5 Consortium HHS Public Access. Nat Methods 9:796\u2013804. https:\/\/doi.org\/10.1038\/nmeth.2016.Wisdom","journal-title":"Nat Methods"},{"key":"460_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-025-11257-z","author":"SZA Sura I Mohammed Ali","year":"2025","unstructured":"Mohammed Ali SZA Sura I (2025) A review of methods for gene regulatory networks reconstruction and analysis. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-025-11257-z","journal-title":"Artif Intell Rev"},{"key":"460_CR19","doi-asserted-by":"publisher","unstructured":"Nihad M, Ramadan F, Mohammed Ali SI (2023) Machine learning methods and approaches for predicting Covid19. In: AIP Conference Proceedings, vol. 2591. https:\/\/doi.org\/10.1063\/5.0119819","DOI":"10.1063\/5.0119819"},{"key":"460_CR20","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1093\/nar\/gkz369","volume":"47","author":"H Peterson","year":"2019","unstructured":"Peterson H, Kolberg L, Kuzmin I, Arak T, Adler P (2019) g: profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res 47:191\u2013198. https:\/\/doi.org\/10.1093\/nar\/gkz369","journal-title":"Nucleic Acids Res"},{"key":"460_CR21","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/0022-5193(66)90013-0","volume":"13","author":"EC Pielou","year":"1966","unstructured":"Pielou EC (1966) The measurement of diversity in different types of biological collections. J Theor Biol 13:131\u2013144. https:\/\/doi.org\/10.1016\/0022-5193(66)90013-0","journal-title":"J Theor Biol"},{"key":"460_CR22","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf315","author":"RT Pop","year":"2025","unstructured":"Pop RT, Hsieh PH, Belova T, Mathelier A, Kuijjer ML (2025) Gene regulatory network integration with multi-omics data enhances survival predictions in cancer. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbaf315","journal-title":"Brief Bioinform"},{"key":"460_CR23","doi-asserted-by":"publisher","unstructured":"AL Raheim Hamza SZ, Lafta LA, Al Rashid HA (2023) Classification of DNA sequence for diabetes mellitus type using machine learning methods. In: ICMETE 2023. In: Lecture Notes in Networks and Systems. Springer, Singapore. vol. l 894. https:\/\/doi.org\/10.1007\/978-981-99-9562-2_8","DOI":"10.1007\/978-981-99-9562-2_8"},{"issue":"1","key":"460_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbac482","volume":"24","author":"S Sebastian","year":"2023","unstructured":"Sebastian S, Roy S, Kalita J (2023) A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer\u2019s disease network. Brief Bioinf 24(1):1\u201314. https:\/\/doi.org\/10.1093\/bib\/bbac482","journal-title":"Brief Bioinf"},{"issue":"6","key":"460_CR25","doi-asserted-by":"publisher","first-page":"3103","DOI":"10.1109\/TCBB.2022.3165092","volume":"19","author":"E Sefer","year":"2022","unstructured":"Sefer E (2022) A data-driven procedure to learn the growth of biological networks. IEEE\/ACM Trans. Comput. Biol. Bioinf. 19(6):3103\u20133113","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"3","key":"460_CR26","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1089\/cmb.2024.0807","volume":"32","author":"E Sefer","year":"2025","unstructured":"Sefer E (2025) DRGAT\u202f: predicting drug responses via diffusion-based graph attention network. J Comput Biol 32(3):330\u2013350. https:\/\/doi.org\/10.1089\/cmb.2024.0807","journal-title":"J Comput Biol"},{"key":"460_CR27","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1093\/nar\/gkj109","volume":"34","author":"C Stark","year":"2006","unstructured":"Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:535\u2013539. https:\/\/doi.org\/10.1093\/nar\/gkj109","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"460_CR28","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41581-024-00849-7","volume":"20","author":"BA Unger Avila P","year":"2024","unstructured":"Unger Avila P BA, Padvitski T, Leote AC, Chen H, Saez-Rodriguez J, Kann M (2024) Gene regulatory networks in age-related decline and disease,\u201d gene regul. networks dis. ageing. Nat Rev Nephrol 20(9):616\u2013633. https:\/\/doi.org\/10.1038\/s41581-024-00849-7","journal-title":"Nat Rev Nephrol"},{"issue":"1","key":"460_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13073-018-0608-4","volume":"10","author":"MGP Van Der Wijst","year":"2018","unstructured":"Van Der Wijst MGP, De Vries DH, Brugge H, Westra HJ, Franke L (2018) An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Med 10(1):1\u201315. https:\/\/doi.org\/10.1186\/s13073-018-0608-4","journal-title":"Genome Med"},{"key":"460_CR30","doi-asserted-by":"publisher","first-page":"126154","DOI":"10.1109\/ACCESS.2019.2936794","volume":"7","author":"N Wani","year":"2019","unstructured":"Wani N, Raza K (2019) IMTF-GRN: integrative matrix tri-factorization for inference of gene regulatory networks. IEEE Access 7:126154\u2013126163. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936794","journal-title":"IEEE Access"},{"key":"460_CR31","unstructured":"Weisstein EW (2000) Gini Coefficient. MathWorld--A Wolfram Resource. https:\/\/mathworld.wolfram.com\/GiniCoefficient.html"},{"key":"460_CR32","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf453","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Jin X, Zhang X (2025) GCNMF-SDA: predicting snoRNA-disease associations based on graph convolution and non-negative matrix factorization. Brief Bioinf. https:\/\/doi.org\/10.1093\/bib\/bbaf453","journal-title":"Brief Bioinf"},{"key":"460_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf349","author":"J Zhou","year":"2025","unstructured":"Zhou J, Gong N, Hu Y, Yu H, Wang G, Wu H (2025) GRANet: a graph residual attention network for gene regulatory network inference. Brief Bioinf. https:\/\/doi.org\/10.1093\/bib\/bbaf349","journal-title":"Brief Bioinf"}],"container-title":["Theory in Biosciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12064-026-00460-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12064-026-00460-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12064-026-00460-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T22:02:49Z","timestamp":1772575369000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12064-026-00460-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,3]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["460"],"URL":"https:\/\/doi.org\/10.1007\/s12064-026-00460-3","relation":{},"ISSN":["1431-7613","1611-7530"],"issn-type":[{"value":"1431-7613","type":"print"},{"value":"1611-7530","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,3]]},"assertion":[{"value":"13 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"10"}}