{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T14:11:57Z","timestamp":1756995117413,"version":"3.41.2"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"China National Natural Science Foundation","doi-asserted-by":"crossref","award":["62172121","82073800"],"award-info":[{"award-number":["62172121","82073800"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province of China","doi-asserted-by":"crossref","award":["LH2022F012"],"award-info":[{"award-number":["LH2022F012"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-\u03b3), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.<\/jats:p>","DOI":"10.1093\/bib\/bbae104","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:43:31Z","timestamp":1711629811000},"source":"Crossref","is-referenced-by-count":4,"title":["Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2950-2560","authenticated-orcid":false,"given":"Zhenyu","family":"Wei","sequence":"first","affiliation":[{"name":"Intelligent Systems Science and Engineering College, Harbin Engineering University , Harbin 150001 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4328-466X","authenticated-orcid":false,"given":"Chengkui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Intelligent Systems Science and Engineering College, Harbin Engineering University , Harbin 150001 , China"},{"name":"Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd , Shanghai , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9928-5189","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intelligent Systems Science and Engineering College, Harbin Engineering University , Harbin 150001 , China"}]},{"given":"Jiayu","family":"Xu","sequence":"additional","affiliation":[{"name":"Intelligent Systems Science and Engineering College, Harbin Engineering University , Harbin 150001 , China"}]},{"given":"Nan","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd , Shanghai , China"},{"name":"School 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