{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:19:24Z","timestamp":1787851164922,"version":"build-2784847793"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133914","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T23:09:45Z","timestamp":1778368185000},"page":"133914","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A hybrid quantum-classical neural network framework for genomic sequence classification"],"prefix":"10.1016","volume":"694","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8983-1436","authenticated-orcid":false,"given":"Riya","family":"Bansal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7268-0207","authenticated-orcid":false,"given":"Nikhil Kumar","family":"Rajput","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4379-1837","authenticated-orcid":false,"given":"Megha","family":"Khanna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133914_bib0005","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1385\/1-59259-901-X:143","article-title":"Structural DNA nanotechnology: an overview","author":"Seeman","year":"2005","journal-title":"NanoBiotechnology Protoc."},{"issue":"1","key":"10.1016\/j.neucom.2026.133914_bib0010","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s41588-018-0295-5","article-title":"A primer on deep learning in genomics","volume":"51","author":"Zou","year":"2019","journal-title":"Nat. Genet."},{"issue":"4","key":"10.1016\/j.neucom.2026.133914_bib0015","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0232391","article-title":"Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study","volume":"15","author":"Randhawa","year":"2020","journal-title":"PLOS ONE"},{"key":"10.1016\/j.neucom.2026.133914_bib0020","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s12859-018-2182-6","article-title":"Deep learning models for bacteria taxonomic classification of metagenomic data","volume":"19","author":"Fiannaca","year":"2018","journal-title":"BMC Bioinform."},{"issue":"6","key":"10.1016\/j.neucom.2026.133914_bib0025","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nrg3920","article-title":"Machine learning applications in genetics and genomics","volume":"16","author":"Libbrecht","year":"2015","journal-title":"Nat. Rev. Genet."},{"issue":"9","key":"10.1016\/j.neucom.2026.133914_bib0030","doi-asserted-by":"crossref","first-page":"4439","DOI":"10.3390\/s23094439","article-title":"Deep learning framework for complex disease risk prediction using genomic variations","volume":"23","author":"Alzoubi","year":"2023","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.neucom.2026.133914_bib0035","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1003711","article-title":"Enhanced regulatory sequence prediction using gapped k-mer features","volume":"10","author":"Ghandi","year":"2014","journal-title":"PLOS Comput. Biol."},{"key":"10.1016\/j.neucom.2026.133914_bib0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2009-10-10-r108","article-title":"Genomic DNA k-mer spectra: models and modalities","volume":"10","author":"Chor","year":"2009","journal-title":"Genome Biol."},{"issue":"6018","key":"10.1016\/j.neucom.2026.133914_bib0045","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1126\/science.1197891","article-title":"On the future of genomic data","volume":"331","author":"Kahn","year":"2011","journal-title":"Science"},{"issue":"10","key":"10.1016\/j.neucom.2026.133914_bib0050","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","article-title":"Effective gene expression prediction from sequence by integrating long-range interactions","volume":"18","author":"Avsec","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.neucom.2026.133914_bib0055","series-title":"Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies","author":"Hochreiter","year":"2001"},{"issue":"7671","key":"10.1016\/j.neucom.2026.133914_bib0060","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2026.133914_bib0065","series-title":"Quantum Computation and Quantum Information","author":"Nielsen","year":"1991"},{"issue":"7747","key":"10.1016\/j.neucom.2026.133914_bib0070","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","article-title":"Supervised learning with quantum-enhanced feature spaces","volume":"567","author":"Havl\u00ed\u010dek","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2026.133914_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2023.107959","article-title":"Quantum gate algorithm for reference-guided DNA sequence alignment","volume":"107","author":"Varsamis","year":"2023","journal-title":"Comput. Biol. Chem."},{"issue":"4","key":"10.1016\/j.neucom.2026.133914_bib0080","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0249850","article-title":"QuASeR: quantum accelerated de novo DNA sequence reconstruction","volume":"16","author":"Sarkar","year":"2021","journal-title":"PLOS ONE"},{"key":"10.1016\/j.neucom.2026.133914_bib0085","author":"Singh"},{"key":"10.1016\/j.neucom.2026.133914_bib0090","first-page":"1531","article-title":"Large scale multiple kernel learning","volume":"7","author":"Sonnenburg","year":"2006","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"10.1016\/j.neucom.2026.133914_bib0095","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning\u2013based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nat. Methods"},{"key":"10.1016\/j.neucom.2026.133914_bib0100","first-page":"1","article-title":"DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning","volume":"18","author":"Angermueller","year":"2017","journal-title":"Genome Biol."},{"key":"10.1016\/j.neucom.2026.133914_bib0105","author":"Liu"},{"issue":"4","key":"10.1016\/j.neucom.2026.133914_bib0110","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1093\/bioinformatics\/btz694","article-title":"Identifying enhancer\u2013promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism","volume":"36","author":"Hong","year":"2020","journal-title":"Bioinformatics"},{"issue":"21","key":"10.1016\/j.neucom.2026.133914_bib0115","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1093\/bioinformatics\/btx431","article-title":"DeepLoc: prediction of protein subcellular localization using deep learning","volume":"33","author":"Almagro Armenteros","year":"2017","journal-title":"Bioinformatics"},{"issue":"9","key":"10.1016\/j.neucom.2026.133914_bib0120","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1038\/nphys3029","article-title":"Quantum principal component analysis","volume":"10","author":"Lloyd","year":"2014","journal-title":"Nat. Phys."},{"issue":"1","key":"10.1016\/j.neucom.2026.133914_bib0125","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s41534-019-0157-8","article-title":"A generative modeling approach for benchmarking and training shallow quantum circuits","volume":"5","author":"Benedetti","year":"2019","journal-title":"NPJ Quantum Inf."},{"key":"10.1016\/j.neucom.2026.133914_bib0130","author":"Lloyd"},{"key":"10.1016\/j.neucom.2026.133914_bib0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113669","article-title":"Enhancing quantum support vector machine for healthcare applications using custom feature maps","volume":"320","author":"Bansal","year":"2025","journal-title":"Knowl.-based Syst."},{"issue":"1","key":"10.1016\/j.neucom.2026.133914_bib0140","doi-asserted-by":"crossref","DOI":"10.1103\/RevModPhys.94.015004","article-title":"Noisy intermediate-scale quantum algorithms","volume":"94","author":"Bharti","year":"2022","journal-title":"Rev. Mod. Phys."},{"key":"10.1016\/j.neucom.2026.133914_bib0145","author":"Ma"},{"issue":"2","key":"10.1016\/j.neucom.2026.133914_bib0150","doi-asserted-by":"crossref","first-page":"106","DOI":"10.58496\/MJCS\/2024\/010","article-title":"QIS-box: pioneering ultralightweight S-box generation with quantum inspiration","volume":"4","author":"Alkateb","year":"2024","journal-title":"Mesop. J. CyberSecur."},{"key":"10.1016\/j.neucom.2026.133914_bib0155","doi-asserted-by":"crossref","first-page":"1","DOI":"10.70470\/SHIFRA\/2023\/004","article-title":"Assessing the vulnerability of quantum cryptography systems to emerging cyber threats","volume":"2023","author":"Burhanuddin","year":"2023","journal-title":"SHIFRA"},{"key":"10.1016\/j.neucom.2026.133914_bib0160","doi-asserted-by":"crossref","first-page":"95","DOI":"10.70470\/KHWARIZMIA\/2023\/009","article-title":"Secure and scalable quantum cryptographic algorithms for next-generation computer networks","volume":"2023","author":"Burhanuddin","year":"2023","journal-title":"KHWARIZMIA"},{"key":"10.1016\/j.neucom.2026.133914_bib0165","doi-asserted-by":"crossref","first-page":"43","DOI":"10.70470\/SHIFRA\/2023\/006","article-title":"A survey of cryptographic algorithms in cybersecurity: from classical methods to quantum-resistant solutions","volume":"2023","author":"Tambe-Jagtap","year":"2023","journal-title":"SHIFRA"},{"key":"10.1016\/j.neucom.2026.133914_bib0170","doi-asserted-by":"crossref","DOI":"10.1109\/TITS.2025.3538786","article-title":"Qnn-vrcs: a quantum neural network for vehicle road cooperation systems","volume":"26","author":"Innan","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133914_bib0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2025.118411","article-title":"Quantum neural network-assisted topology optimization: concept and implementation with parameterized quantum circuits","volume":"448","author":"Sukulthanasorn","year":"2026","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.neucom.2026.133914_bib0180","doi-asserted-by":"crossref","first-page":"226","DOI":"10.22331\/q-2020-02-06-226","article-title":"Data re-uploading for a universal quantum classifier","volume":"4","author":"P\u00e9rez-Salinas","year":"2020","journal-title":"Quantum"},{"issue":"2","key":"10.1016\/j.neucom.2026.133914_bib0195","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0171410","article-title":"Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks","volume":"12","author":"Umarov","year":"2017","journal-title":"PLOS ONE"},{"issue":"1","key":"10.1016\/j.neucom.2026.133914_bib0200","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s12863-023-01123-8","article-title":"Genomic benchmarks: a collection of datasets for genomic sequence classification","volume":"24","author":"Gre\u0161ov\u00e1","year":"2023","journal-title":"BMC Genom. Data"},{"key":"10.1016\/j.neucom.2026.133914_bib0205","article-title":"Enhancer identification using transfer and adversarial deep learning of DNA sequences","author":"Cohn","year":"2018","journal-title":"Biorxiv"},{"key":"10.1016\/j.neucom.2026.133914_bib0210","series-title":"Seminars in Nuclear Medicine","first-page":"283","article-title":"Basic principles of ROC analysis","volume":"vol. 8","author":"Metz","year":"1978"},{"key":"10.1016\/j.neucom.2026.133914_bib0215","author":"Simonyan"},{"key":"10.1016\/j.neucom.2026.133914_bib0220","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2026.133914_bib0225","first-page":"2825","article-title":"Scikit-Learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2026.133914_bib0230","series-title":"International Conference on Discovery Science","first-page":"32","article-title":"Hyperparameter importance of quantum neural networks across small datasets","author":"Moussa","year":"2022"},{"key":"10.1016\/j.neucom.2026.133914_bib0235","series-title":"Applied Logistic Regression","author":"Hosmer Jr","year":"2013"},{"key":"10.1016\/j.neucom.2026.133914_bib0240","article-title":"Wilcoxon signed-rank test","volume":"8","author":"Woolson","year":"2005","journal-title":"Encycl. biostat."},{"key":"10.1016\/j.neucom.2026.133914_bib0245","series-title":"Quantum Computation and Quantum Information","author":"Nielsen","year":"2010"},{"issue":"12","key":"10.1016\/j.neucom.2026.133914_bib0250","doi-asserted-by":"crossref","DOI":"10.1002\/qute.201900070","article-title":"Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms","volume":"2","author":"Sim","year":"2019","journal-title":"Adv. Quantum Technol."},{"issue":"3","key":"10.1016\/j.neucom.2026.133914_bib0255","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevA.71.032313","article-title":"Average fidelity between random quantum states","volume":"71","author":"\u017byczkowski","year":"2005","journal-title":"Phys. Rev. A\u2014At. Mol. Opt. Phys."},{"issue":"1","key":"10.1016\/j.neucom.2026.133914_bib0260","doi-asserted-by":"crossref","first-page":"4812","DOI":"10.1038\/s41467-018-07090-4","article-title":"Barren plateaus in quantum neural network training landscapes","volume":"9","author":"McClean","year":"2018","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.neucom.2026.133914_bib0265","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1038\/s41467-021-21728-w","article-title":"Cost function dependent barren plateaus in shallow parametrized quantum circuits","volume":"12","author":"Cerezo","year":"2021","journal-title":"Nat. Commun."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013111?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013111?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:26:59Z","timestamp":1787848019000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226013111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":51,"alternative-id":["S0925231226013111"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133914","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A hybrid quantum-classical neural network framework for genomic sequence classification","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133914","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133914"}}