{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:04:03Z","timestamp":1780934643900,"version":"3.54.1"},"reference-count":79,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004514","name":"Yunnan University of Finance and Economics","doi-asserted-by":"publisher","award":["2024D44"],"award-info":[{"award-number":["2024D44"]}],"id":[{"id":"10.13039\/501100004514","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005273","name":"Yunnan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["202401AT070279"],"award-info":[{"award-number":["202401AT070279"]}],"id":[{"id":"10.13039\/501100005273","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005273","name":"Yunnan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["202401AT070280"],"award-info":[{"award-number":["202401AT070280"]}],"id":[{"id":"10.13039\/501100005273","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005273","name":"Yunnan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["202501AT070455"],"award-info":[{"award-number":["202501AT070455"]}],"id":[{"id":"10.13039\/501100005273","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202415"],"award-info":[{"award-number":["62202415"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132319","type":"journal-article","created":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:17:02Z","timestamp":1775315822000},"page":"132319","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["RGGE-DTD: A Unified Model for Simultaneous Prediction of Drug-Target Interactions and Drug-Disease Associations in Drug Repositioning"],"prefix":"10.1016","volume":"321","author":[{"given":"Junrong","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Heng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichang","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinpeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132319_b0005","article-title":"Computational Drug Design in the AI Era: A Systematic Review of Molecular Representations, Generative Architectures, and Performance Assessment","volume":"100095","author":"Abbasi","year":"2025","journal-title":"Pharmacological Reviews"},{"key":"10.1016\/j.eswa.2026.132319_b0010","series-title":"In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"2623","article-title":"Optuna: A next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"10.1016\/j.eswa.2026.132319_b0015","doi-asserted-by":"crossref","first-page":"46","DOI":"10.2174\/1874364100802010046","article-title":"Topiramate induced acute angle closure glaucoma","volume":"2","author":"Aminlari","year":"2008","journal-title":"The Open Ophthalmology Journal"},{"key":"10.1016\/j.eswa.2026.132319_b0020","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/bib\/bbz157","article-title":"Machine learning approaches and databases for prediction of drug\u2013target interaction: A survey paper","volume":"22","author":"Bagherian","year":"2021","journal-title":"Briefings in bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0025","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1093\/bioinformatics\/btq112","article-title":"A machine learning approach to predicting protein\u2013ligand binding affinity with applications to molecular docking","volume":"26","author":"Ballester","year":"2010","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0030","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1136\/jmg.2008.061820","article-title":"Epigenetic mutations of the imprinted IGF2-H19 domain in Silver\u2013Russell syndrome (SRS): Results from a large cohort of patients with SRS and SRS-like phenotypes","volume":"46","author":"Bartholdi","year":"2009","journal-title":"Journal of medical genetics"},{"key":"10.1016\/j.eswa.2026.132319_b0035","doi-asserted-by":"crossref","first-page":"18120","DOI":"10.1074\/jbc.M601791200","article-title":"Novel mutants of the human \u03b21-adrenergic receptor reveal amino acids relevant for receptor activation","volume":"281","author":"Behr","year":"2006","journal-title":"Journal of Biological Chemistry"},{"key":"10.1016\/j.eswa.2026.132319_b0040","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s11916-003-0011-7","article-title":"Ergotamine and dihydroergotamine: A review","volume":"7","author":"Bigal","year":"2003","journal-title":"Current Pain and Headache Reports"},{"key":"10.1016\/j.eswa.2026.132319_b0045","series-title":"Advances in neural information processing systems","first-page":"26","article-title":"Translating embeddings for modeling multi-relational data","author":"Bordes","year":"2013"},{"key":"10.1016\/j.eswa.2026.132319_b0050","series-title":"StatPearls [Internet]","article-title":"Neuromuscular blockade","author":"Cook","year":"2023"},{"key":"10.1016\/j.eswa.2026.132319_b0055","doi-asserted-by":"crossref","first-page":"D1104","DOI":"10.1093\/nar\/gks994","article-title":"The Comparative Toxicogenomics Database: Update 2013","volume":"41","author":"Davis","year":"2012","journal-title":"Nucleic Acids Research"},{"key":"10.1016\/j.eswa.2026.132319_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120754","article-title":"TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function","volume":"232","author":"Dehghan","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132319_b0065","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/toxsci\/kfv311","article-title":"Human sulfotransferases enhance the cytotoxicity of tolvaptan","volume":"150","author":"Fang","year":"2016","journal-title":"Toxicological sciences"},{"key":"10.1016\/j.eswa.2026.132319_b0070","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/msb.2011.26","article-title":"PREDICT: A method for inferring novel drug indications with application to personalized medicine","volume":"7","author":"Gottlieb","year":"2011","journal-title":"Molecular systems biology"},{"key":"10.1016\/j.eswa.2026.132319_b0075","series-title":"In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","first-page":"855","article-title":"node2vec: Scalable feature learning for networks","author":"Grover","year":"2016"},{"key":"10.1016\/j.eswa.2026.132319_b0080","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/nau.22681","article-title":"Effect of short\u2010and long\u2010term sildenafil treatment on erectile dysfunction in rats with partial bladder outlet obstruction","volume":"35","author":"Gur","year":"2016","journal-title":"Neurourology and Urodynamics"},{"key":"10.1016\/j.eswa.2026.132319_b0085","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/194178b0","article-title":"Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients","volume":"194","author":"Hansch","year":"1962","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2026.132319_b0090","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13321-017-0209-z","article-title":"SimBoost: A read-across approach for predicting drug\u2013target binding affinities using gradient boosting machines","volume":"9","author":"He","year":"2017","journal-title":"Journal of cheminformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0095","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1038\/ng.292","article-title":"15q13. 3 microdeletions increase risk of idiopathic generalized epilepsy","volume":"41","author":"Helbig","year":"2009","journal-title":"Nature genetics"},{"key":"10.1016\/j.eswa.2026.132319_b0100","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3389\/fbioe.2020.00218","article-title":"Predicting drug-disease associations via multi-task learning based on collective matrix factorization","volume":"8","author":"Huang","year":"2020","journal-title":"Frontiers in Bioengineering and Biotechnology"},{"key":"10.1016\/j.eswa.2026.132319_b0105","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TKDE.2005.50","article-title":"Using AUC and accuracy in evaluating learning algorithms","volume":"17","author":"Huang","year":"2005","journal-title":"IEEE Transactions on knowledge and Data Engineering"},{"key":"10.1016\/j.eswa.2026.132319_b0110","unstructured":"Huang, Z., Kosan, M., Silva, A., & Singh, A. (2023). Link prediction without graph neural networks. arXiv preprint arXiv:2305.13656."},{"key":"10.1016\/j.eswa.2026.132319_b0115","series-title":"In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","first-page":"685","article-title":"A broader picture of random-walk based graph embedding","author":"Huang","year":"2021"},{"key":"10.1016\/j.eswa.2026.132319_b0120","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-40072-y","article-title":"Computational approaches for predicting drug-disease associations: A comprehensive review","volume":"19","author":"Huang","year":"2025","journal-title":"Frontiers of Computer Science"},{"key":"10.1016\/j.eswa.2026.132319_b0125","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nbt1284","article-title":"Relating protein pharmacology by ligand chemistry","volume":"25","author":"Keiser","year":"2007","journal-title":"Nature biotechnology"},{"key":"10.1016\/j.eswa.2026.132319_b0130","series-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"Kendall","year":"2018"},{"key":"10.1016\/j.eswa.2026.132319_b0135","doi-asserted-by":"crossref","first-page":"D767","DOI":"10.1093\/nar\/gkn892","article-title":"Human protein reference database\u20142009 update","volume":"37","author":"Keshava Prasad","year":"2009","journal-title":"Nucleic Acids Research"},{"key":"10.1016\/j.eswa.2026.132319_b0140","series-title":"1-D convolutional neural networks for signal processing applications","first-page":"8360","author":"Kiranyaz","year":"2019"},{"key":"10.1016\/j.eswa.2026.132319_b0145","doi-asserted-by":"crossref","DOI":"10.1177\/00368504221109215","article-title":"Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction","volume":"105","author":"Kitsiranuwat","year":"2022","journal-title":"Science Progress"},{"key":"10.1016\/j.eswa.2026.132319_b0150","doi-asserted-by":"crossref","first-page":"D1035","DOI":"10.1093\/nar\/gkq1126","article-title":"DrugBank 3.0: A comprehensive resource for \u2018Omics\u2019 research on drugs","volume":"39","author":"Knox","year":"2010","journal-title":"Nucleic Acids Research"},{"key":"10.1016\/j.eswa.2026.132319_b0155","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1097\/00000542-199312000-00009","article-title":"Naloxone, meperidine, and shivering","volume":"79","author":"Kurz","year":"1993","journal-title":"Anesthesiology"},{"key":"10.1016\/j.eswa.2026.132319_b0160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0014-2999(82)90171-6","article-title":"The interaction of pancuronium with cardiac and ileal muscarinic receptors","volume":"80","author":"Leung","year":"1982","journal-title":"European journal of pharmacology"},{"key":"10.1016\/j.eswa.2026.132319_b0165","doi-asserted-by":"crossref","first-page":"924","DOI":"10.3389\/fchem.2019.00924","article-title":"Identification of drug-disease associations using information of molecular structures and clinical symptoms via deep convolutional neural network","volume":"7","author":"Li","year":"2020","journal-title":"Frontiers in Chemistry"},{"key":"10.1016\/j.eswa.2026.132319_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126509","article-title":"Drug-target interactions prediction based on network topology feature representation embedded deep forest","volume":"551","author":"Lian","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.132319_b0175","series-title":"CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning","first-page":"707","author":"Lin","year":"2024"},{"key":"10.1016\/j.eswa.2026.132319_b0180","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","article-title":"Link prediction in complex networks: A survey","volume":"390","author":"L\u00fc","year":"2011","journal-title":"Physica A: Statistical mechanics and its applications"},{"key":"10.1016\/j.eswa.2026.132319_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.bcp.2023.115954","article-title":"Gestational exposure to bisphenol AF causes endocrine disorder of corpus luteum by altering ovarian SIRT-1\/Nrf2\/NF-kB expressions and macrophage proangiogenic function in mice","volume":"220","author":"Lu","year":"2024","journal-title":"Biochemical Pharmacology"},{"key":"10.1016\/j.eswa.2026.132319_b0190","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1093\/bioinformatics\/bty013","article-title":"Computational drug repositioning using low-rank matrix approximation and randomized algorithms","volume":"34","author":"Luo","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0195","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nature communications"},{"key":"10.1016\/j.eswa.2026.132319_b0200","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/2193-9616-1-17","article-title":"Drug-target and disease networks: Polypharmacology in the post-genomic era","volume":"1","author":"Masoudi-Nejad","year":"2013","journal-title":"In silico pharmacology"},{"key":"10.1016\/j.eswa.2026.132319_b0205","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s00204-022-03390-3","article-title":"Exposure to acetaminophen impairs gametogenesis and fertility in zebrafish (Danio rerio)","volume":"97","author":"Moreira","year":"2023","journal-title":"Archives of Toxicology"},{"key":"10.1016\/j.eswa.2026.132319_b0210","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"Journal of computational chemistry"},{"key":"10.1016\/j.eswa.2026.132319_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110442","article-title":"DFT_ANPD: A dual-feature two-sided attention network for anticancer natural products detection","volume":"194","author":"Norouzi","year":"2025","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2026.132319_b0220","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ejphar.2005.11.025","article-title":"Role of periaqueductal grey prostaglandin receptors in formalin-induced hyperalgesia","volume":"530","author":"Oliva","year":"2006","journal-title":"European journal of pharmacology"},{"key":"10.1016\/j.eswa.2026.132319_b0225","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: Deep drug\u2013target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0230","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1186\/s12859-020-03677-1","article-title":"A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network","volume":"21","author":"Peng","year":"2020","journal-title":"BMC bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0235","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa430","article-title":"An end-to-end heterogeneous graph representation learning-based framework for drug\u2013target interaction prediction","volume":"22","author":"Peng","year":"2021","journal-title":"Briefings in bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0240","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1359\/jbmr.1998.13.3.454","article-title":"Homologous up-regulation of vitamin D receptors is tissue specific in the rat","volume":"13","author":"Rc, g.","year":"1998","journal-title":"J Bone Miner Res"},{"key":"10.1016\/j.eswa.2026.132319_b0245","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1002\/ajmg.a.35372","article-title":"Prenatal ablation of nicotinic receptor alpha7 cell lineages produces lumbosacral spina bifida the severity of which is modified by choline and nicotine exposure","volume":"158","author":"Rogers","year":"2012","journal-title":"American Journal of Medical Genetics Part A"},{"key":"10.1016\/j.eswa.2026.132319_b0250","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1093\/bioinformatics\/btab826","article-title":"A network-based drug repurposing method via non-negative matrix factorization","volume":"38","author":"Sadeghi","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0255","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PloS one"},{"key":"10.1016\/j.eswa.2026.132319_b0260","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/nrd.2016.230","article-title":"A comprehensive map of molecular drug targets","volume":"16","author":"Santos","year":"2017","journal-title":"Nature reviews Drug discovery"},{"key":"10.1016\/j.eswa.2026.132319_b0265","series-title":"Modeling relational data with graph convolutional networks","first-page":"593","author":"Schlichtkrull","year":"2018"},{"key":"10.1016\/j.eswa.2026.132319_b0270","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1039\/C5MB00650C","article-title":"An improved approach for predicting drug\u2013target interaction: Proteochemometrics to molecular docking","volume":"12","author":"Shaikh","year":"2016","journal-title":"Molecular Biosystems"},{"key":"10.1016\/j.eswa.2026.132319_b0275","series-title":"SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions","first-page":"1636","author":"Shi","year":"2015"},{"key":"10.1016\/j.eswa.2026.132319_b0280","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1124\/jpet.111.179879","article-title":"Pentazocine-induced antinociception is mediated mainly by \u03bc-opioid receptors and compromised by \u03ba-opioid receptors in mice","volume":"338","author":"Shu","year":"2011","journal-title":"The Journal of pharmacology and experimental therapeutics"},{"key":"10.1016\/j.eswa.2026.132319_b0285","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.sbi.2017.10.010","article-title":"Protein structure-based drug design: From docking to molecular dynamics","volume":"48","author":"\u015aled\u017a","year":"2018","journal-title":"Current opinion in structural biology"},{"key":"10.1016\/j.eswa.2026.132319_b0290","series-title":"Advances in neural information processing systems","first-page":"29","article-title":"Improved deep metric learning with multi-class n-pair loss objective","author":"Sohn","year":"2016"},{"key":"10.1016\/j.eswa.2026.132319_b0295","article-title":"AMGDTI: Drug\u2013target interaction prediction based on adaptive meta-graph learning in heterogeneous network","volume":"25","author":"Su","year":"2024","journal-title":"Briefings in bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0300","doi-asserted-by":"crossref","unstructured":"Tanvir, F., Saifuddin, K. M., Hossain, T., Bagavathi, A., & Akbas, E. (2023). HeTriNet: Heterogeneous graph triplet attention network for drug-target-disease interaction. arXiv preprint arXiv:2312.00189.","DOI":"10.1109\/DSAA61799.2024.10722832"},{"key":"10.1016\/j.eswa.2026.132319_b0305","series-title":"In Proceedings of the 2020 12th international conference on machine learning and computing","first-page":"580","article-title":"RA-GCN: Relational aggregation graph convolutional network for knowledge graph completion","author":"Tian","year":"2020"},{"key":"10.1016\/j.eswa.2026.132319_b0310","doi-asserted-by":"crossref","DOI":"10.1177\/20420188231173327","article-title":"Tolvaptan for the treatment of the syndrome of inappropriate antidiuresis (SIAD)","volume":"14","author":"Tzoulis","year":"2023","journal-title":"Therapeutic Advances in Endocrinology and Metabolism"},{"key":"10.1016\/j.eswa.2026.132319_b0315","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","article-title":"NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions","volume":"35","author":"Wan","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0320","series-title":"Drug-target and Drug-disease Association Prediction based on Drug-target-disease Network and Multi-task Learning","first-page":"930","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132319_b0330","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1021\/acs.jcim.4c01528","article-title":"MMPD-DTA: Integrating multi-modal deep learning with pocket-drug graphs for drug-target binding affinity prediction","volume":"65","author":"Wang","year":"2025","journal-title":"Journal of Chemical Information and Modeling"},{"key":"10.1016\/j.eswa.2026.132319_b0335","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1752-0509-4-S2-S6","article-title":"Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces","volume":"4","author":"Xia","year":"2010","journal-title":"BMC systems biology"},{"key":"10.1016\/j.eswa.2026.132319_b0340","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1016\/j.jacc.2015.06.1329","article-title":"The Classically Cardioprotective Agent Diazoxide Elicits Arrhythmias in Type 2 Diabetes Mellitus","volume":"66","author":"Xie","year":"2015","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.eswa.2026.132319_b0345","series-title":"Reluplex made more practical: Leaky ReLU","first-page":"1","author":"Xu","year":"2020"},{"key":"10.1016\/j.eswa.2026.132319_b0350","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1021\/jm9805945","article-title":"N-Methyl-5-tert-butyltryptamine: A novel, highly potent 5-HT1D receptor agonist","volume":"42","author":"Xu","year":"1999","journal-title":"Journal of medicinal chemistry"},{"key":"10.1016\/j.eswa.2026.132319_b0355","doi-asserted-by":"crossref","first-page":"705","DOI":"10.3390\/cells8070705","article-title":"Convolutional neural network and bidirectional long short-term memory-based method for predicting drug\u2013disease associations","volume":"8","author":"Xuan","year":"2019","journal-title":"Cells"},{"key":"10.1016\/j.eswa.2026.132319_b0360","unstructured":"Yang, B., Yih, W.-t., He, X., Gao, J., & Deng, L. (2014). Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575."},{"key":"10.1016\/j.eswa.2026.132319_b0365","article-title":"Predicting drug-disease associations with heterogeneous network embedding. Chaos: An Interdisciplinary","volume":"29","author":"Yang","year":"2019","journal-title":"Journal of Nonlinear Science"},{"key":"10.1016\/j.eswa.2026.132319_b0370","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btz331","article-title":"Drug repositioning based on bounded nuclear norm regularization","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0375","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa267","article-title":"Computational drug repositioning based on multi-similarities bilinear matrix factorization","volume":"22","author":"Yang","year":"2021","journal-title":"Briefings in bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0380","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102805","article-title":"GCNGAT: Drug\u2013disease association prediction based on graph convolution neural network and graph attention network","volume":"150","author":"Yang","year":"2024","journal-title":"Artificial Intelligence in Medicine"},{"key":"10.1016\/j.eswa.2026.132319_b0385","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa243","article-title":"Predicting drug\u2013disease associations through layer attention graph convolutional network","volume":"22","author":"Yu","year":"2021","journal-title":"Briefings in bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0390","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1093\/bioinformatics\/btz418","article-title":"deepDR: A network-based deep learning approach to in silico drug repositioning","volume":"35","author":"Zeng","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.eswa.2026.132319_b0395","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1016\/S0022-3565(24)38271-0","article-title":"\u03b12C adrenoceptors inhibit adenylyl cyclase in mouse striatum: Potential activation by dopamine","volume":"289","author":"Zhang","year":"1999","journal-title":"The Journal of pharmacology and experimental therapeutics"},{"key":"10.1016\/j.eswa.2026.132319_b0400","article-title":"HINGRL: Predicting drug\u2013disease associations with graph representation learning on heterogeneous information networks","volume":"23","author":"Zhao","year":"2022","journal-title":"Briefings in bioinformatics"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426012327?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426012327?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:56:31Z","timestamp":1780934191000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426012327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":79,"alternative-id":["S0957417426012327"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132319","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RGGE-DTD: A Unified Model for Simultaneous Prediction of Drug-Target Interactions and Drug-Disease Associations in Drug Repositioning","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132319","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132319"}}