[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124221-en":3,"doc-seo-124221-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124221,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Predicting the S. cerevisiae Gene Expression Score by a Machine Learning Classifier - Article","Gene expression activity is quantified via an expression score (ES) that reflects gene importance for cellular processes and can vary with many attributes. A random forest machine-learning classifier was selected, trained, and optimized on the WEKA platform to predict the ES of Saccharomyces cerevisiae genes using curated and balanced data from the Saccharomyces Genome Database (SGD). The model evaluates attribute significance and identifies key factors spanning experimental conditions plus genetic, physical, statistical, and logistic features, achieving 84.1% correctness on an unseen test set.","Article  \nPredicting the S. cerevisiae Gene Expression Score by a Machine Learning Classifier  \nPiotr H. Pawłowski and Piotr Zielenkiewicz  \n[https://doi.org/10.3390/life15050723](https://doi.org/10.3390/life15050723)  \nArticle  \nPredicting the S. cerevisiae Gene Expression Score by a Machine Learning Classi􀀂er  \nPiotr H. Paw􀀇owski 1, * and Piotr Zielenkiewicz 1,2  \nAcademic Editor: Christian Lehmann  \nReceived: 9 April 2025  \nRevised: 27 April 2025  \nAccepted: 28 April 2025  \nPublished: 29 April 2025  \nCitation: Paw􀀇owski, P.H.;  \nZielenkiewicz, P. Predicting the S. cerevisiae Gene Expression Score by a Machine Learning Classi􀀂er. Life 2025, 15, 723. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)life15050723  \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 Biochemistry and Biophysics, Polish Academy of Sciences, 02-093 Warsaw, Poland; [piotr@ibb.waw.pl](piotr@ibb.waw.pl)  \n2 Laboratory of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, Faculty of Biology, University of Warsaw, 02-096 Warsaw, Poland  \n* [Correspondence: piotrp@ibb.waw.pl](Correspondence: piotrp@ibb.waw.pl); Tel.: +48-22-659-70-72  \nAbstract: The topic of this work is gene expression and its score according to various factors analyzed globally using machine learning techniques. The expression score (ES) of genes characterizes their activity and, thus, their importance for cellular processes. This may depend on many different factors (attributes) . To 􀀂nd the most important classi􀀂er, a machine learning classi􀀂er (random forest) was selected, trained, and optimized on the Waikato Environment for Knowledge Analysis WEKA platform, resulting in the most accurate attribute-dependent prediction of the ES of Saccharomyces cerevisiae genes. In this way, data from the Saccharomyces Genome Database (SGD), presenting ES values corresponding to a wide spectrum of attributes, were used, revised, classi􀀂ed, and balanced, and the signi􀀂cance of the considered attributes was evaluated. In this way, the novel random forest model indicates the most important attributes determining classes of low, moderate, and high ES. They cover both the experimental conditions and the genetic, physical, statistical, and logistic features. During validation, the obtained model could classify the instances of a primary unknown test set with a correctness of 84.1% .  \nKeywords: gene expression score; Saccharomyces cerevisiae; random forest classi􀀂er; machine learning; AI  \n1. Introduction  \nGene expression is a manifestation of a gene’s role in a cell, usually through the processes of RNA transcription and protein synthesis [1] . It is, therefore, a manifestation of the “movement” of the biochemical machinery in the process of life. Gene expression may be divided into the following stages: signal transduction, chromatin remodeling, transcription, posttranscriptional modi􀀂cation, RNA transport, translation, and mRNA degradation. There are many views on what controls protein expression, which can be studied [2] . At the level of molecular biology, three determinants of this phenomenon are widely recognized [3], i.e., transcriptional regulation, e.g., by transcription factors and structural DNA properties [4]; the modulation of the transcription machinery, e.g., by accessory factors [5] and ligands [6]; and epigenetic structural in􀀃uence, e.g., involving the chromatin remodeling process [7] . All of these are focal points of interest in cell biology and have recently been intensively investigated, mainly because of the development of large repositories that collect exabytes of gene expression data.  \nThe measurement of the activity of t","cbCaih6YYDyeMQux","https://ap.wps.com/l/cbCaih6YYDyeMQux","pdf",1073369,1,19,"English","en",105,"# Introduction\n## Gene expression and stages\n## Expression score (ES) as a quantitative metric\n## Predicting ES using machine learning","[{\"question\":\"What does the expression score (ES) represent in this study?\",\"answer\":\"ES characterizes gene activity and indicates the gene’s importance for cellular processes. It is derived as a log2 ratio based on normalized detection amounts.\"},{\"question\":\"Which machine learning method is used to predict the ES values?\",\"answer\":\"The study uses a random forest classifier, trained and optimized within the WEKA platform for attribute-dependent ES prediction.\"},{\"question\":\"What kinds of features does the model identify as most important?\",\"answer\":\"The model highlights attributes determining low, moderate, and high ES classes, including experimental conditions and genetic, physical, statistical, and logistic features.\"}]","Predicting the S. cerevisiae Gene Expression Score by a Machine Learning Classifier - Article | PDF",1785821089,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-the-s-cerevisiae-gene-expression-score-by-a-machine-learning-classifier-article","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-s-cerevisiae-gene-expression-score-by-a-machine-learning-classifier-article/124221/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the expression score (ES) represent in this study?","Question",{"text":76,"@type":77},"ES characterizes gene activity and indicates the gene’s importance for cellular processes. It is derived as a log2 ratio based on normalized detection amounts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning method is used to predict the ES values?",{"text":81,"@type":77},"The study uses a random forest classifier, trained and optimized within the WEKA platform for attribute-dependent ES prediction.",{"name":83,"@type":74,"acceptedAnswer":84},"What kinds of features does the model identify as most important?",{"text":85,"@type":77},"The model highlights attributes determining low, moderate, and high ES classes, including experimental conditions and genetic, physical, statistical, and logistic features.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},"General","general"]