PMTIDMA: plant miRNA target interaction prediction framework based on denoising autoencoder and multi-scale spectral graph convolutional attention

PMTIDMA: plant miRNA target interaction prediction framework based on denoising autoencoder and multi-scale spectral graph convolutional attention | 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 Research Article PMTIDMA: plant miRNA target interaction prediction framework based on denoising autoencoder and multi-scale spectral graph convolutional attention Jiangqing Wang, Xin Zhou, Jin Xie, Jun Tie, Kexin Hu, Yuwei Wan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9127892/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate prediction of plant miRNA–target interactions(MTIs) is fundamental to understanding gene regulation in plant growth, development, and stress responses. Experimental validation of MTIs is accurate but costly, time-consuming, and limited in scalability, especially in plant species. In contrast, computation-based methods can efficiently mine potential regulatory relationships, but existing models still exhibit deficiencies in feature representation capability, structural information modeling, and performance under limited-sample conditions. This paper proposes PMTIDMA, a novel plant miRNA–target gene prediction framework based on graph neural networks. It first uses a Denoising Autoencoder(DAE) to reduce dimensionality of miRNA and mRNA sequence features, enhancing robustness and reducing noise. Then, heterogeneous graphs are built from miRNA–miRNA and mRNA–mRNA sequence similarities and known miRNA–mRNA interactions. On this basis, the sequence features and heterogeneous graphs are jointly input into the multi-scale spectral graph convolutional network for joint training. This network performs weighted fusion on the convolution results of spectral graphs of different scales through the attention mechanism, and further combines the standard Graph convolutional network layer and the Jumping Knowledge module to fully capture the local and global structural information. Ultimately, the multi-layer perceptron was used to predict the interaction probability between miRNA and target genes. Experimental results on multiple plant datasets show that PMTIDMA consistently outperforms existing mainstream methods. Specifically, PMTIDMA achieves an AUPRC of 0.9302, an AUROC of 0.9369, and an F1-score of 0.9351. The ablation experiment further verified the effectiveness of the denoising autoencoder, multi-scale spectral graph convolution and attention fusion mechanism in the model. In conclusion, PMTIDMA provides an efficient and robust computational framework for predicting the interaction between plant mirnas and target genes, which is conducive to the systematic analysis of the plant miRNA regulatory network. Heterogeneous graph DAE multi-scale spectral graph convolution scale-level attention fusion JK module Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9127892","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619261544,"identity":"faf7f189-a886-41b5-a47d-7fc382949303","order_by":0,"name":"Jiangqing Wang","email":"","orcid":"","institution":"South-Central Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Jiangqing","middleName":"","lastName":"Wang","suffix":""},{"id":619261545,"identity":"d9a45d4a-7e37-46aa-b464-0ba93f9e2755","order_by":1,"name":"Xin Zhou","email":"","orcid":"","institution":"South-Central Minzu 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Experimental validation of MTIs is accurate but costly, time-consuming, and limited in scalability, especially in plant species. In contrast, computation-based methods can efficiently mine potential regulatory relationships, but existing models still exhibit deficiencies in feature representation capability, structural information modeling, and performance under limited-sample conditions. This paper proposes PMTIDMA, a novel plant miRNA\u0026ndash;target gene prediction framework based on graph neural networks. It first uses a Denoising Autoencoder(DAE) to reduce dimensionality of miRNA and mRNA sequence features, enhancing robustness and reducing noise. Then, heterogeneous graphs are built from miRNA\u0026ndash;miRNA and mRNA\u0026ndash;mRNA sequence similarities and known miRNA\u0026ndash;mRNA interactions. On this basis, the sequence features and heterogeneous graphs are jointly input into the multi-scale spectral graph convolutional network for joint training. This network performs weighted fusion on the convolution results of spectral graphs of different scales through the attention mechanism, and further combines the standard Graph convolutional network layer and the Jumping Knowledge module to fully capture the local and global structural information. Ultimately, the multi-layer perceptron was used to predict the interaction probability between miRNA and target genes. Experimental results on multiple plant datasets show that PMTIDMA consistently outperforms existing mainstream methods. Specifically, PMTIDMA achieves an AUPRC of 0.9302, an AUROC of 0.9369, and an F1-score of 0.9351. The ablation experiment further verified the effectiveness of the denoising autoencoder, multi-scale spectral graph convolution and attention fusion mechanism in the model. In conclusion, PMTIDMA provides an efficient and robust computational framework for predicting the interaction between plant mirnas and target genes, which is conducive to the systematic analysis of the plant miRNA regulatory network.\u003c/p\u003e","manuscriptTitle":"PMTIDMA: plant miRNA target interaction prediction framework based on denoising autoencoder and multi-scale spectral graph convolutional attention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 22:29:53","doi":"10.21203/rs.3.rs-9127892/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01a35349-3ba7-4730-92e3-c35796b448df","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T07:27:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 22:29:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9127892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9127892","identity":"rs-9127892","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}