{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T08:32:00Z","timestamp":1768638720222,"version":"3.49.0"},"reference-count":0,"publisher":"IGI Global","issue":"1","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,6]]},"abstract":"<p>From the experimental results, quantum convolutional regression against neural network combined with incremental feature selection method shows significant advantages in gene cell structure detection. In terms of random accuracy, the recognition accuracy of this method for multiple gene image data sets is significantly higher than that of traditional methods, indicating that it can capture the characteristic information of gene cell structure more accurately.Gene expression is the process by which the proteins necessary to determine an organism's physical traits are produced, encompassing two main stages: transcription, in which enzymes transfer genetic information from DNA to RNA, and translation, in which RNA is converted into proteins and other essential biomolecules. While various methods can extract gene expression data from DNA or RNA, traditional extraction and analysis techniques often suffer from low accuracy and inefficiency in detecting nanoscale gene cell structures, limiting their usefulness in advanced biological imaging research. To address this challenge, we propose a quantum deep learning approach for bioimaging-based gene cell structure detection integrated with nanomaterial analysis. Gene images are collected and analyzed to simulate nanomaterial structures, and a quantum convolutional regression adversarial neural network is employed to accurately capture subtle nanoscale features\u2014such as cell edge contours, internal textures, and unique morphologies resulting from interactions with nanomaterials\u2014through quantum computing. The incorporation of an adversarial learning mechanism further reduces detection errors and enhances recognition accuracy for complex cell structures. A ranked feature list was compiled, key features were selected using an incremental feature selection strategy, and performance was evaluated across multiple gene image datasets using metrics such as random accuracy, sensitivity, AUC, F-measure, Dice coefficient, and NSE. Experimental results show that our method significantly outperforms conventional approaches, achieving higher recognition accuracy and more precise characterization of gene cell structures.<\/p>","DOI":"10.4018\/ijitsa.390275","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T16:25:40Z","timestamp":1759767940000},"page":"1-20","source":"Crossref","is-referenced-by-count":0,"title":["Nanomaterial Analysis-Based Bioimaging for Gene Cell Structure Modeling Using Quantum-Inspired Deep Learning Techniques"],"prefix":"10.4018","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1298-0696","authenticated-orcid":true,"given":"Quan","family":"Gui","sequence":"first","affiliation":[{"name":"Northeastern University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","container-title":["International Journal of Information Technologies and Systems Approach"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=390275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T20:59:51Z","timestamp":1768597191000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJITSA.390275"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2025,10,6]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"URL":"https:\/\/doi.org\/10.4018\/ijitsa.390275","relation":{},"ISSN":["1935-570X","1935-5718"],"issn-type":[{"value":"1935-570X","type":"print"},{"value":"1935-5718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,6]]}}}