[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290818-105":59,"doc-detail-290818-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","evaluating-genetic-regulators-of-micrornas-using-machine-learning-models","Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models","","This study explores the genetic regulators of microRNAs (miRNAs) by using a set of machine learning models to predict miRNA expression levels from gene expression data. The approach accurately predicts 353 human miRNAs (R2 > 0.5), and coefficient analysis identifies genetic regulators while emphasizing multifactorial miRNA regulation. Network and pathway-filtered analyses reveal pathway-associated miRNAs, producing a curated list of miRNAs and regulators plus metrics and coefficients, with a filtered subnetwork linked to pathways and phenotypes, and supporting TCGA-based custom modeling.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-genetic-regulators-of-micrornas-using-machine-learning-models/290818/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-genetic-regulators-of-micrornas-using-machine-learning-models/290818.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study predict miRNA expression from gene expression data?","Question",{"text":112,"@type":113},"It uses a set of machine learning models that map gene expression inputs to miRNA expression levels.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What evidence indicates the predictive models perform well?",{"text":117,"@type":113},"The models accurately predict the expression of 353 human miRNAs with R2 values greater than 0.5.",{"name":119,"@type":110,"acceptedAnswer":120},"How are genetic regulators of miRNAs identified in this work?",{"text":121,"@type":113},"The study analyzes the coefficients of the predictive models to infer regulators associated with each miRNA.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290818,1789642628,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Article  \nEvaluating Genetic Regulators of MicroRNAs Using Machine Learning Models  \nMert Cihan 1, Uchenna Alex Anyaegbunam 1, Steffen Albrecht 2, Miguel A. Andrade-Navarro 1,3 and Maximilian Sprang 1,3,4, *  \nAcademic Editor: Fuyi Li  \nReceived: 5 May 2025  \nRevised: 6 June 2025  \nAccepted: 13 June 2025  \nPublished: 16 June 2025  \nCitation: Cihan, M.; Anyaegbunam, U.A.; Albrecht, S.; Andrade-Navarro, M.A.; Sprang, M. Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models. Int. J. Mol. Sci. 2025, 26, 5757. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/ijms26125757](10.3390/ijms26125757)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany; [mercihan@uni-mainz.de](mercihan@uni-mainz.de) (M.C.); andrade@uni-mainz.de (M.A.A.-N.)  \n2 Department of General Practice and Primary Health Care, Faculty of Medical and Health Sciences (FMHS), The University of Auckland, Auckland 1023, New Zealand  \n3 Institute of Quantitative and Computational Biology, Johannes Gutenberg University Mainz,  \n55128 Mainz, Germany  \n4 Department of Dermatology, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany  \n* Correspondence: masprang@uni-mainz.de  \nAbstract: This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R2 > 0.5), revealing robust miRNA–gene regulatory relationships. By analyzing the coefficients of these predictive models, we identified genetic regulators for each miRNA and highlighted the multifactorial nature of miRNA regulation. Further network analysis uncovered that miRNAs with higher predictive accuracy are more densely connected to their top predictive genes, reflecting strong regulatory control within miRNA–gene networks. To refine these insights, we filtered the miRNA–gene interaction networks to identify miRNAs specifically associated with enriched pathways, such as synaptic function and cardiovascular processes. From this pathway-centric analysis, we present a curated list of miRNAs and their genetic regulators, pinpointing their activity within distinct biological contexts. Additionally, our study provides a comprehensive set of metrics and coefficients for the genes most predictive of miRNA expression, along with a filtered subnetwork of miRNAs linked to specific pathways and phenotypes. By integrating miRNA expression predictors with network analysis and pathway enrichment, this work advances our understanding of miRNA regulatory mechanisms and their roles across distinct biological systems. Our approach enables researchers to train custom models using TCGA data and predict miRNA expression from gene expression inputs.  \nKeywords: microRNA; machine learning; gene expression modeling; regulatory networks; functional genomics  \n1. Introduction  \nMicroRNAs (miRNAs) play a critical role in the regulation of gene expression by binding to target messenger RNAs (mRNAs) and either promoting their degradation or inhibiting their translation [1] . These small, non-coding RNAs are involved in a wide array of cellular processes, including development, differentiation, and apoptosis, making them essential for maintaining cellular homeostasis [2,3] .  \nAccurate profiling of miRNA expression is crucial for understanding miRNA functions. To predict miRNA targets, among other methods, the negative correlation between miRNA  \nand mRNA expression is used to identify ","cbCaiiTqlzqcFnYi","https://ap.wps.com/l/cbCaiiTqlzqcFnYi","pdf",1622018,17,"English","# Abstract\n# Introduction","[{\"question\":\"How does the study predict miRNA expression from gene expression data?\",\"answer\":\"It uses a set of machine learning models that map gene expression inputs to miRNA expression levels.\"},{\"question\":\"What evidence indicates the predictive models perform well?\",\"answer\":\"The models accurately predict the expression of 353 human miRNAs with R2 values greater than 0.5.\"},{\"question\":\"How are genetic regulators of miRNAs identified in this work?\",\"answer\":\"The study analyzes the coefficients of the predictive models to infer regulators associated with each miRNA.\"}]","Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models | PDF",43]