DAG-VAERL: a novel causal inference method for building causal gene regulatory network

DAG-VAERL: a novel causal inference method for building causal gene regulatory network | 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 DAG-VAERL: a novel causal inference method for building causal gene regulatory network Teng Long, Sachit Satyal, Jean Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8245170/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Causal discovery methods provide a powerful tool for uncovering the causal relationships between lncRNAs and target genes in gene regulations. In many causal inference and structure learning tasks, learning the Directed Acyclic Graph (DAG) structure from data is a challenging problem. Traditional DAG learning methods often rely on heuristic searches or strict constraints, which fail to effectively handle complex nonlinear relationships and discrete data.To address this, we propose a novel deep generative model — DAG-VAERL, which combines Graph Neural Networks (GNN) as well as Reinforcement Learning (RL) frameworks and Graph Attention Networks (GAT) module, leveraging Variational Autoencoders (VAE) to learn the DAG structure. DAG-VAERL is capable of modeling complex dependencies between nodes through GNNs and optimizing the graph structure using RL strategies. We conduct extensive experiments on synthetic and real-world datasets, including Alzheimer's disease data, to validate the superiority of DAG-VAERL in structural discovery and parameter estimation. Experimental results demonstrate that DAG-VAERL significantly outperforms traditional methods in structure recovery, especially when dealing with complex data involving nonlinear and discrete variables. This model not only effectively learns the DAG structure from data but also serves as a powerful tool for causal inference and other graph-bard analysis tasks, providing a new approach for related fields. Causality Directed acyclic graph Deep learning Reinforcement Learning Long non-coding RNAs Alzheimer's disease Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviews received at journal 30 Jan, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 30 Nov, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8245170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578226416,"identity":"d5862856-964a-427d-8637-af812ced1b5d","order_by":0,"name":"Teng Long","email":"","orcid":"","institution":"The University of Texas at Arlington","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Long","suffix":""},{"id":578226418,"identity":"564508a8-bdc7-4919-b981-69a9e35d48a3","order_by":1,"name":"Sachit Satyal","email":"","orcid":"","institution":"The University of Texas at 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