{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:41:05Z","timestamp":1777585265076,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Shaanxi Provincial Innovation Capacity Support Program","award":["2024CX-GXPT-44"],"award-info":[{"award-number":["2024CX-GXPT-44"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-025-06186-1","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T07:29:49Z","timestamp":1752650989000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution"],"prefix":"10.1186","volume":"26","author":[{"given":"Jiaqi","family":"Xiong","sequence":"first","affiliation":[]},{"given":"Nan","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Shiyang","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Haoyang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yingxu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Duo","family":"Ai","sequence":"additional","affiliation":[]},{"given":"Jingjie","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"issue":"1","key":"6186_CR1","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1186\/s13059-022-02660-8","volume":"23","author":"A Janjic","year":"2022","unstructured":"Janjic A, Wange LE, Bagnoli JW, Geuder J, Nguyen P, Richter D, Vieth B, Vick B, Jeremias I, Ziegenhain C. Prime-seq, efficient and powerful bulk rna sequencing. Genome Biol. 2022;23(1):88.","journal-title":"Genome Biol"},{"issue":"14","key":"6186_CR2","doi-asserted-by":"publisher","first-page":"4936","DOI":"10.1073\/pnas.0408031102","volume":"102","author":"M Levine","year":"2005","unstructured":"Levine M, Davidson EH. Gene regulatory networks for development. Proc Natl Acad Sci. 2005;102(14):4936\u201342.","journal-title":"Proc Natl Acad Sci"},{"key":"6186_CR3","doi-asserted-by":"crossref","unstructured":"Dong J, Li J, Wang F. Deep learning in gene regulatory network inference: a survey. IEEE\/ACM Trans Comput Biol Bioinform (2024)","DOI":"10.1109\/TCBB.2024.3442536"},{"issue":"4","key":"6186_CR4","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1136\/gutjnl-2020-321397","volume":"71","author":"B Kloesch","year":"2022","unstructured":"Kloesch B, Ionasz V, Paliwal S, Hruschka N, Villarreal JM, \u00d6llinger R, Mueller S, Dienes HP, Schindl M, Gruber ES. A gata6-centred gene regulatory network involving hnfs and $$\\delta $$np63 controls plasticity and immune escape in pancreatic cancer. Gut. 2022;71(4):766\u201377.","journal-title":"Gut"},{"issue":"5","key":"6186_CR5","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1093\/bib\/bbae380","volume":"25","author":"Z Meng","year":"2024","unstructured":"Meng Z, Liu S, Liang S, Jani B, Meng Z. Heterogeneous biomedical entity representation learning for gene-disease association prediction. Brief Bioinform. 2024;25(5):380.","journal-title":"Brief Bioinform"},{"issue":"9","key":"6186_CR6","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1038\/s41576-020-0239-7","volume":"21","author":"JA Goldman","year":"2020","unstructured":"Goldman JA, Poss KD. Gene regulatory programmes of tissue regeneration. Nat Rev Genet. 2020;21(9):511\u201325.","journal-title":"Nat Rev Genet"},{"issue":"4","key":"6186_CR7","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1016\/j.celrep.2017.10.001","volume":"21","author":"AR Sonawane","year":"2017","unstructured":"Sonawane AR, Platig J, Fagny M, Chen C-Y, Paulson JN, Lopes-Ramos CM, DeMeo DL, Quackenbush J, Glass K, Kuijjer ML. Understanding tissue-specific gene regulation. Cell Rep. 2017;21(4):1077\u201388.","journal-title":"Cell Rep"},{"issue":"5","key":"6186_CR8","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1093\/bib\/bbae355","volume":"25","author":"X Yi","year":"2024","unstructured":"Yi X, Liu S, Wu Y, McCloskey D, Meng Z. Bpp: a platform for automatic biochemical pathway prediction. Brief Bioinform. 2024;25(5):355.","journal-title":"Brief Bioinform"},{"key":"6186_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.compbiolchem.2015.04.012","volume":"59","author":"F Salleh","year":"2015","unstructured":"Salleh F, Arif SM, Zainudin S, Firdaus-Raih M. Reconstructing gene regulatory networks from knock-out data using gaussian noise model and pearson correlation coefficient. Comput Biol Chem. 2015;59:3\u201314.","journal-title":"Comput Biol Chem"},{"issue":"8","key":"6186_CR10","doi-asserted-by":"publisher","first-page":"1005024","DOI":"10.1371\/journal.pcbi.1005024","volume":"12","author":"F Liu","year":"2016","unstructured":"Liu F, Zhang S-W, Guo W-F, Wei Z-G, Chen L. Inference of gene regulatory network based on local bayesian networks. PLoS Comput Biol. 2016;12(8):1005024.","journal-title":"PLoS Comput Biol"},{"key":"6186_CR11","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s12864-017-4228-y","volume":"18","author":"L Xing","year":"2017","unstructured":"Xing L, Guo M, Liu X, Wang C, Wang L, Zhang Y. An improved bayesian network method for reconstructing gene regulatory network based on candidate auto selection. BMC Genomics. 2017;18:17\u201330.","journal-title":"BMC Genomics"},{"key":"6186_CR12","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.compbiolchem.2016.08.002","volume":"64","author":"K Raza","year":"2016","unstructured":"Raza K, Alam M. Recurrent neural network based hybrid model for reconstructing gene regulatory network. Comput Biol Chem. 2016;64:322\u201334.","journal-title":"Comput Biol Chem"},{"key":"6186_CR13","unstructured":"Ju W, Yi S, Wang Y, Xiao Z, Mao Z, Li H, Gu Y, Qin Y, Yin N, Wang S et al. A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges. arXiv preprint arXiv:2403.04468 (2024)"},{"issue":"D1","key":"6186_CR14","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1093\/nar\/gkab1053","volume":"50","author":"K Katz","year":"2022","unstructured":"Katz K, Shutov O, Lapoint R, Kimelman M, Brister JR, O\u2019Sullivan C. The sequence read archive: a decade more of explosive growth. Nucleic Acids Res. 2022;50(D1):387\u201390.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6186_CR15","doi-asserted-by":"publisher","first-page":"5357","DOI":"10.1038\/s41467-022-32887-9","volume":"13","author":"Y Shiraishi","year":"2022","unstructured":"Shiraishi Y, Okada A, Chiba K, Kawachi A, Omori I, Mateos RN, Iida N, Yamauchi H, Kosaki K, Yoshimi A. Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data. Nat Commun. 2022;13(1):5357.","journal-title":"Nat Commun"},{"issue":"5","key":"6186_CR16","doi-asserted-by":"publisher","first-page":"2853","DOI":"10.1109\/TCBB.2023.3282212","volume":"20","author":"Z Gao","year":"2023","unstructured":"Gao Z, Tang J, Xia J, Zheng C-H, Wei P-J. Cnngrn: A convolutional neural network-based method for gene regulatory network inference from bulk time-series expression data. IEEE\/ACM Trans Comput Biol Bioinf. 2023;20(5):2853\u201361.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"6186_CR17","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1016\/j.csbj.2020.10.022","volume":"18","author":"J Wang","year":"2020","unstructured":"Wang J, Ma A, Ma Q, Xu D, Joshi T. Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks. Comput Struct Biotechnol J. 2020;18:3335\u201343.","journal-title":"Comput Struct Biotechnol J"},{"issue":"3","key":"6186_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568953","volume":"41","author":"S Liu","year":"2023","unstructured":"Liu S, Meng Z, Macdonald C, Ounis I. Graph neural pre-training for recommendation with side information. ACM Trans Inf Syst. 2023;41(3):1\u201328.","journal-title":"ACM Trans Inf Syst"},{"key":"6186_CR19","doi-asserted-by":"crossref","unstructured":"Liu S, Ounis I, Macdonald C. An mlp-based algorithm for efficient contrastive graph recommendations. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022;2431\u20132436","DOI":"10.1145\/3477495.3531874"},{"key":"6186_CR20","unstructured":"Yin N, Shen L, Wang M, Lan L, Ma Z, Chen C, Hua X-S, Luo X. Coco: A coupled contrastive framework for unsupervised domain adaptive graph classification. In: International Conference on Machine Learning, 2023;40040\u201340053. PMLR"},{"key":"6186_CR21","unstructured":"Wang Y, Yin N, Xiao M, Yi X, Liu S, Liang S. Dusego: Dual second-order equivariant graph ordinary differential equation. arXiv preprint arXiv:2411.10000 (2024)"},{"key":"6186_CR22","unstructured":"Wang Y, Liang V, Yin N, Liu S, Segal E. SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification (2024). https:\/\/arxiv.org\/abs\/2412.16276"},{"issue":"1","key":"6186_CR23","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1186\/s12859-023-05441-7","volume":"24","author":"S Liang","year":"2023","unstructured":"Liang S, Liu S, Song J, Lin Q, Zhao S, Li S, Li J, Liang S, Wang J. Hmcda: a novel method based on the heterogeneous graph neural network and metapath for circrna-disease associations prediction. BMC Bioinform. 2023;24(1):335. https:\/\/doi.org\/10.1186\/s12859-023-05441-7.","journal-title":"BMC Bioinform"},{"issue":"5","key":"6186_CR24","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1093\/bib\/bbad317","volume":"24","author":"Z Wang","year":"2023","unstructured":"Wang Z, Liang S, Liu S, Meng Z, Wang J, Liang S. Sequence pre-training-based graph neural network for predicting lncrna\u2013mirna associations. Brief Bioinform. 2023;24(5):317. https:\/\/doi.org\/10.1093\/bib\/bbad317.","journal-title":"Brief Bioinform"},{"key":"6186_CR25","doi-asserted-by":"crossref","unstructured":"Wei P-J, Guo Z, Gao Z, Ding Z, Cao R-F, Su Y, Zheng C-H. Inference of gene regulatory networks based on directed graph convolutional networks. Brief Bioinform 2024;25(4)","DOI":"10.1093\/bib\/bbae309"},{"key":"6186_CR26","unstructured":"Tong Z, Liang Y, Sun C, Rosenblum DS, Lim A. Directed graph convolutional network. arXiv preprint arXiv:2004.13970 (2020)"},{"key":"6186_CR27","doi-asserted-by":"crossref","unstructured":"Pol AA, Berger V, Germain C, Cerminara G, Pierini M. Anomaly detection with conditional variational autoencoders. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), 2019;1651\u20131657. IEEE","DOI":"10.1109\/ICMLA.2019.00270"},{"issue":"3","key":"6186_CR28","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1093\/bib\/bbae143","volume":"25","author":"Z Gao","year":"2024","unstructured":"Gao Z, Su Y, Xia J, Cao R-F, Ding Y, Zheng C-H, Wei P-J. Deepfgrn: inference of gene regulatory network with regulation type based on directed graph embedding. Brief Bioinform. 2024;25(3):143.","journal-title":"Brief Bioinform"},{"key":"6186_CR29","unstructured":"Ke Z, Yu H, Li J, Zhang H. DUPLEX: Dual GAT for Complex Embedding of Directed Graphs (2024). https:\/\/arxiv.org\/abs\/2406.05391"},{"key":"6186_CR30","unstructured":"Wang Y, Liu S, Wang M, Liang S, Yin N. Degree distribution based spiking graph networks for domain adaptation. arXiv preprint arXiv:2410.06883 (2024)"},{"key":"6186_CR31","unstructured":"Meng Z, Meng Z, Yuan K, Ounis I. FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction (2024). https:\/\/arxiv.org\/abs\/2406.01651"},{"issue":"5","key":"6186_CR32","doi-asserted-by":"publisher","first-page":"2853","DOI":"10.1109\/TCBB.2023.3282212","volume":"20","author":"Z Gao","year":"2023","unstructured":"Gao Z, Tang J, Xia J, Zheng C-H, Wei P-J. Cnngrn: A convolutional neural network-based method for gene regulatory network inference from bulk time-series expression data. IEEE\/ACM Trans Comput Biol Bioinf. 2023;20(5):2853\u201361. https:\/\/doi.org\/10.1109\/TCBB.2023.3282212.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"4","key":"6186_CR33","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/bib\/bbae309","volume":"25","author":"P-J Wei","year":"2024","unstructured":"Wei P-J, Guo Z, Gao Z, Ding Z, Cao R-F, Su Y, Zheng C-H. Inference of gene regulatory networks based on directed graph convolutional networks. Brief Bioinform. 2024;25(4):309. https:\/\/doi.org\/10.1093\/bib\/bbae309.","journal-title":"Brief Bioinform"},{"key":"6186_CR34","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. CoRR (2016) arxiv:1609.02907"},{"key":"6186_CR35","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? CoRR (2018) arxiv:1810.00826"},{"key":"6186_CR36","unstructured":"Ying C, Cai T, Luo S, Zheng S, Ke G, He D, Shen Y, Liu T-Y. Do transformers really perform badly for graph representation? In: Thirty-Fifth Conference on Neural Information Processing Systems (2021). https:\/\/openreview.net\/forum?id=OeWooOxFwDa"},{"issue":"3","key":"6186_CR37","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/ni0302-221","volume":"3","author":"M Karin","year":"2002","unstructured":"Karin M, Lin A. Nf-$$\\kappa $$b at the crossroads of life and death. Nat Immunol. 2002;3(3):221\u20137.","journal-title":"Nat Immunol"},{"issue":"18","key":"6186_CR38","doi-asserted-by":"publisher","first-page":"10386","DOI":"10.1073\/pnas.151257998","volume":"98","author":"DK Biswas","year":"2001","unstructured":"Biswas DK, Dai S-C, Cruz A, Weiser B, Graner E, Pardee AB. The nuclear factor kappa b (nf-$$\\kappa $$b): a potential therapeutic target for estrogen receptor negative breast cancers. Proc Natl Acad Sci. 2001;98(18):10386\u201391.","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"6186_CR39","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1002\/1878-0261.12162","volume":"12","author":"Y Zhao","year":"2018","unstructured":"Zhao Y, Ma J, Fan Y, Wang Z, Tian R, Ji W, Zhang F, Niu R. Tgf-$$\\beta $$ transactivates egfr and facilitates breast cancer migration and invasion through canonical smad3 and erk\/sp1 signaling pathways. Mol Oncol. 2018;12(3):305\u201321.","journal-title":"Mol Oncol"},{"issue":"6","key":"6186_CR40","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1177\/1947601910378691","volume":"1","author":"J Xu","year":"2010","unstructured":"Xu J, Chen Y, Olopade OI. Myc and breast cancer. Genes & cancer. 2010;1(6):629\u201340.","journal-title":"Genes & cancer"},{"issue":"1","key":"6186_CR41","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1186\/s12885-022-10393-x","volume":"22","author":"P Zhu","year":"2022","unstructured":"Zhu P, Liu G, Wang X, Lu J, Zhou Y, Chen S, Gao Y, Wang C, Yu J, Sun Y. Transcription factor c-jun modulates glut1 in glycolysis and breast cancer metastasis. BMC Cancer. 2022;22(1):1283.","journal-title":"BMC Cancer"},{"issue":"3","key":"6186_CR42","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1002\/humu.10174","volume":"21","author":"A-L B\u00f8rresen-Dale","year":"2003","unstructured":"B\u00f8rresen-Dale A-L. Tp53 and breast cancer. Hum Mutat. 2003;21(3):292\u2013300.","journal-title":"Hum Mutat"},{"key":"6186_CR43","doi-asserted-by":"crossref","unstructured":"Mirzaei S, Ranjbar B, Tackallou SH, Aref AR. Hypoxia inducible factor-1$$\\alpha $$ (hif-1$$\\alpha $$) in breast cancer: The crosstalk with oncogenic and onco-suppressor factors in regulation of cancer hallmarks. Pathol Res Pract 2023;154676","DOI":"10.1016\/j.prp.2023.154676"},{"issue":"11","key":"6186_CR44","first-page":"14619","volume":"8","author":"C-Y Wei","year":"2015","unstructured":"Wei C-Y, Tan Q-X, Zhu X, Qin Q-H, Zhu F-B, Mo Q-G, Yang W-P. Expression of cdkn1a\/p21 and tgfbr2 in breast cancer and their prognostic significance. Int J Clin Exp Pathol. 2015;8(11):14619.","journal-title":"Int J Clin Exp Pathol"},{"issue":"7","key":"6186_CR45","doi-asserted-by":"publisher","first-page":"23358","DOI":"10.1002\/jbt.23358","volume":"37","author":"D Chang","year":"2023","unstructured":"Chang D, Li L, Xu Z, Chen X. Targeting fos attenuates malignant phenotypes of breast cancer: Evidence from in silico and in vitro studies. J Biochem Mol Toxicol. 2023;37(7):23358.","journal-title":"J Biochem Mol Toxicol"},{"issue":"40","key":"6186_CR46","doi-asserted-by":"publisher","first-page":"65797","DOI":"10.18632\/oncotarget.11667","volume":"7","author":"J-R Jhan","year":"2016","unstructured":"Jhan J-R, Andrechek ER. Stat3 accelerates myc induced tumor formation while reducing growth rate in a mouse model of breast cancer. Oncotarget. 2016;7(40):65797.","journal-title":"Oncotarget"},{"issue":"3","key":"6186_CR47","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.celrep.2018.12.071","volume":"26","author":"A Santoro","year":"2019","unstructured":"Santoro A, Vlachou T, Luzi L, Melloni G, Mazzarella L, D\u2019Elia E, Aobuli X, Pasi CE, Reavie L, Bonetti P. p53 loss in breast cancer leads to myc activation, increased cell plasticity, and expression of a mitotic signature with prognostic value. Cell Rep. 2019;26(3):624\u201338.","journal-title":"Cell Rep"},{"issue":"9","key":"6186_CR48","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.1073\/pnas.0808042106","volume":"106","author":"M Sachdeva","year":"2009","unstructured":"Sachdeva M, Zhu S, Wu F, Wu H, Walia V, Kumar S, Elble R, Watabe K, Mo Y-Y. p53 represses c-myc through induction of the tumor suppressor mir-145. Proc Natl Acad Sci. 2009;106(9):3207\u201312.","journal-title":"Proc Natl Acad Sci"},{"key":"6186_CR49","doi-asserted-by":"crossref","unstructured":"Chen M, Xiao C, Jiang W, Yang W, Qin Q, Tan Q, Lian B, Liang Z, Wei C. Capsaicin inhibits proliferation and induces apoptosis in breast cancer by down-regulating fbi-1-mediated nf-$$\\kappa $$b pathway. Drug Design Dev Therapy 2021;125\u2013140","DOI":"10.2147\/DDDT.S269901"},{"issue":"3","key":"6186_CR50","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1002\/ijc.29812","volume":"138","author":"JJ Hendrikx","year":"2016","unstructured":"Hendrikx JJ, Lagas JS, Song J-Y, Rosing H, Schellens JH, Beijnen JH, Rottenberg S, Schinkel AH. Ritonavir inhibits intratumoral docetaxel metabolism and enhances docetaxel antitumor activity in an immunocompetent mouse breast cancer model. Int J Cancer. 2016;138(3):758\u201369.","journal-title":"Int J Cancer"},{"issue":"21","key":"6186_CR51","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1016\/j.lfs.2013.09.029","volume":"93","author":"H Cheng","year":"2013","unstructured":"Cheng H, Lee SH, Wu S. Effects of n-acetyl-l-cysteine on adhesive strength between breast cancer cell and extracellular matrix proteins after ionizing radiation. Life Sci. 2013;93(21):798\u2013803.","journal-title":"Life Sci"},{"key":"6186_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13046-018-0926-9","volume":"37","author":"Y Peng","year":"2018","unstructured":"Peng Y, Wang Y, Tang N, Sun D, Lan Y, Yu Z, Zhao X, Feng L, Zhang B, Jin L. Andrographolide inhibits breast cancer through suppressing cox-2 expression and angiogenesis via inactivation of p300 signaling and vegf pathway. J Exp Clin Cancer Res. 2018;37:1\u201314.","journal-title":"J. Exp Clin Cancer Res"},{"issue":"2","key":"6186_CR53","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1002\/jcp.20588","volume":"207","author":"N Normanno","year":"2006","unstructured":"Normanno N, Luca AD, Maiello MR, Campiglio M, Napolitano M, Mancino M, Carotenuto A, Viglietto G, Menard S. The mek\/mapk pathway is involved in the resistance of breast cancer cells to the egfr tyrosine kinase inhibitor gefitinib. J Cell Physiol. 2006;207(2):420\u20137.","journal-title":"J Cell Physiol"},{"key":"6186_CR54","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s10549-021-06332-2","volume":"189","author":"MC Batenburg","year":"2021","unstructured":"Batenburg MC, Maarse W, Leij F, Baas IO, Boonstra O, Lansdorp N, Doeksen A, Bongard DH, Verkooijen HM. The impact of hyperbaric oxygen therapy on late radiation toxicity and quality of life in breast cancer patients. Breast Cancer Res Treat. 2021;189:425\u201333.","journal-title":"Breast Cancer Res Treat"},{"issue":"2","key":"6186_CR55","first-page":"1266","volume":"16","author":"S Hu","year":"2018","unstructured":"Hu S, Xu Y, Meng L, Huang L, Sun H. Curcumin inhibits proliferation and promotes apoptosis of breast cancer cells. Exp Ther Med. 2018;16(2):1266\u201372.","journal-title":"Exp Ther Med"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06186-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06186-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06186-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T11:52:25Z","timestamp":1757245945000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06186-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,16]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6186"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06186-1","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,16]]},"assertion":[{"value":"13 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no Conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"179"}}