Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles | 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 Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles Jovana Paunovic Pantic, Danijela Vucevic, Tatjana Radosavljevic, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3911185/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This study focuses on the development of machine learning models based on the features of the run length matrix (RLM) and wavelet analyses, with the potential to detect subtle alterations in hepatocyte chromatin organization due to iron oxide nanoparticle exposure. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Computational biology and bioinformatics/Machine learning Machine Learning Nucleus Random Forest Gradient Boosting Toxicology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2024 Reviews received at journal 25 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviews received at journal 26 Mar, 2024 Reviewers agreed at journal 14 Mar, 2024 Reviewers invited by journal 13 Mar, 2024 Editor assigned by journal 13 Mar, 2024 Editor invited by journal 11 Feb, 2024 Submission checks completed at journal 11 Feb, 2024 First submitted to journal 30 Jan, 2024 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-3911185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272287920,"identity":"867968e2-1c00-4ae2-9f33-8997e69a709d","order_by":0,"name":"Jovana Paunovic Pantic","email":"","orcid":"","institution":"University of Belgrade","correspondingAuthor":false,"prefix":"","firstName":"Jovana","middleName":"Paunovic","lastName":"Pantic","suffix":""},{"id":272287921,"identity":"38dcf692-aa69-48a3-874a-4eba3352b6ff","order_by":1,"name":"Danijela Vucevic","email":"","orcid":"","institution":"University of Belgrade","correspondingAuthor":false,"prefix":"","firstName":"Danijela","middleName":"","lastName":"Vucevic","suffix":""},{"id":272287922,"identity":"bda72030-3127-4c38-aa6c-bb7b67075fdc","order_by":2,"name":"Tatjana Radosavljevic","email":"","orcid":"","institution":"University of Belgrade","correspondingAuthor":false,"prefix":"","firstName":"Tatjana","middleName":"","lastName":"Radosavljevic","suffix":""},{"id":272287923,"identity":"87757d4b-e251-4ea9-a744-aafa087029f9","order_by":3,"name":"Peter R. 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