Synthetic Expansion of Gene Expression Data: Enhancing Predictive Modeling through Augmentation Techniques

Synthetic Expansion of Gene Expression Data: Enhancing Predictive Modeling through Augmentation Techniques | 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 Synthetic Expansion of Gene Expression Data: Enhancing Predictive Modeling through Augmentation Techniques Anthony N. Guarino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6968473/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background Gene expression microarrays offer valuable insights into disease mechanisms, yet their utility for predictive modeling is often constrained by small sample sizes. This study investigates the application of synthetic data augmentation techniques—particularly Gaussian noise injection—to expand two gene expression datasets (GDS3952 and GDS2771) and enhance model performance. Methods Feature selection methods, including ANOVA, Tukey tests, and Linear Discriminant Analysis, were used to reduce dimensionality and isolate discriminative gene subsets. Ensemble classifiers (Random Forest, Bagging, and Voting) were trained on both original and augmented datasets. Gaussian augmentation yielded statistically similar distributions as verified by Kolmogorov–Smirnov tests and significantly improved predictive performance across several tasks. Conclusions Synthetic data augmentation using Gaussian noise led to measurable improvements in model accuracy, with performance gains of up to 6% across tasks. In particular, binary classification of disease status ('healthy' vs. 'cancer') achieved accuracies as high as 98% under Leave-One-Out Cross-Validation (LOOCV). Permutation testing confirmed that these results were not artifacts of overfitting, but reflected the models' ability to learn generalizable patterns from the data. Importantly, the augmented datasets preserved statistical properties of the original data, as demonstrated by non-significant Kolmogorov–Smirnov tests. These findings suggest that even straightforward augmentation techniques, when applied with appropriate validation, can enhance both the accuracy and reliability of predictive models trained on limited gene expression datasets. This approach may be especially valuable in biomedical domains where data collection is costly or constrained. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics gene expression data augmentation Gaussian noise machine learning biomedical data classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 Sep, 2025 Reviews received at journal 17 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor assigned by journal 05 Aug, 2025 Editor invited by journal 24 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 24 Jun, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6968473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496225354,"identity":"922f65ef-d437-400e-8af4-748215d1ab0e","order_by":0,"name":"Anthony N. 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This study investigates the application of synthetic data augmentation techniques\u0026mdash;particularly Gaussian noise injection\u0026mdash;to expand two gene expression datasets (GDS3952 and GDS2771) and enhance model performance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFeature selection methods, including ANOVA, Tukey tests, and Linear Discriminant Analysis, were used to reduce dimensionality and isolate discriminative gene subsets. Ensemble classifiers (Random Forest, Bagging, and Voting) were trained on both original and augmented datasets. Gaussian augmentation yielded statistically similar distributions as verified by Kolmogorov\u0026ndash;Smirnov tests and significantly improved predictive performance across several tasks.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSynthetic data augmentation using Gaussian noise led to measurable improvements in model accuracy, with performance gains of up to 6% across tasks. 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