LncRNA-Cervical Cancer Association Prediction Based on Multi-View Variational Autoencoder Driven by Knowledge Distillation Transfer Learning

LncRNA-Cervical Cancer Association Prediction Based on Multi-View Variational Autoencoder Driven by Knowledge Distillation Transfer Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article LncRNA-Cervical Cancer Association Prediction Based on Multi-View Variational Autoencoder Driven by Knowledge Distillation Transfer Learning Min Yuan, Xiariwayna Abbas, Qian Zhuo, Runmei Guo, Yandan Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7730156/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Long non-coding RNAs (lncRNAs) play crucial regulatory roles in the pathogenesis of cervical cancer. Accurate identification of lncRNA-cervical cancer associations is of great significance for understanding disease molecular mechanisms and discovering potential therapeutic targets. However, the extreme scarcity of cervical cancer-specific lncRNA association data has become the core bottleneck constraining the performance of existing computational prediction methods. To address this challenge, we propose a Multi-View Collaborative Variational Autoencoder based on Knowledge Distillation Transfer Learning (MVCVAE), which innovatively integrates the two technical advantages of multi-source heterogeneous data fusion and cross-disease knowledge transfer. The multi-view collaborative variational autoencoder constructs dual-view feature representations of lncRNA similarity and disease similarity, utilizing symmetric encoder-decoder architecture to learn complex lncRNA-disease association patterns in latent space. The knowledge distillation transfer learning framework adopts a teacher-student network design, where the teacher model is pre-trained on a large-scale dataset containing 12,865 lncRNA-disease associations to learn universal association patterns, and effectively transfers rich prior knowledge to the student model targeting 92 cervical cancer associations through feature distillation and prediction distillation strategies. In five-fold cross-validation, MVCVAE achieved an AUC of 0.9228±0.0058. Through the synergistic effect of multi-view heterogeneous information fusion and cross-domain knowledge transfer, MVCVAE achieves excellent performance in lncRNA-cervical cancer association prediction, effectively alleviating the bottleneck constraint of cervical cancer-specific lncRNA association data scarcity. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Long non-coding RNA Cervical cancer Variational autoencoder Knowledge distillation transfer learning Association prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 26 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Editor invited by journal 25 Nov, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 03 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7730156","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":559355162,"identity":"2d41e089-4dc6-4e8c-b072-d2ecc33cdd61","order_by":0,"name":"Min Yuan","email":"","orcid":"","institution":"Xinjiang Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Yuan","suffix":""},{"id":559355174,"identity":"84483f4e-fb93-4605-9450-082ae9f5fd4e","order_by":1,"name":"Xiariwayna Abbas","email":"","orcid":"","institution":"Xinjiang Medical University Cancer 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Accurate identification of lncRNA-cervical cancer associations is of great significance for understanding disease molecular mechanisms and discovering potential therapeutic targets. However, the extreme scarcity of cervical cancer-specific lncRNA association data has become the core bottleneck constraining the performance of existing computational prediction methods. To address this challenge, we propose a Multi-View Collaborative Variational Autoencoder based on Knowledge Distillation Transfer Learning (MVCVAE), which innovatively integrates the two technical advantages of multi-source heterogeneous data fusion and cross-disease knowledge transfer. The multi-view collaborative variational autoencoder constructs dual-view feature representations of lncRNA similarity and disease similarity, utilizing symmetric encoder-decoder architecture to learn complex lncRNA-disease association patterns in latent space. 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