[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125952-en":3,"doc-seo-125952-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125952,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer","Objective biomarkers are crucial for early diagnosis to support timely treatment and improve survival rates for colorectal cancer (CRC). This study investigates whether piwi-RNAs (piRNAs) and their transcripts can serve as biomarkers by selecting 13 differently expressed piRNAs from previously published CRC serum data. A machine-learning pipeline generates 1020 sequence descriptors and uses Naive Bayes Multinomial classification to identify 27 influential descriptors with 96.4% accuracy. Independent validation on known CRC associations from piRBase yields 85.7% accuracy, and testing on unrelated disease piRNAs produces 44.4% scores, supporting discriminatory biomarker performance.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \npiRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer.  \nPermalink  \n[https://escholarship.org/uc/item/5j80f2f8](https://escholarship.org/uc/item/5j80f2f8)  \nJournal  \nMolecules, 29(18)  \nAuthors  \nLi, Sienna Kouznetsova, Valentina Kesari, Santosh  \net al.  \nPublication Date  \n2024-09-11  \nDOI  \n10.3390/molecules29184311  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n molecules   \nCommunication  \npiRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer  \nSienna Li 1, Valentina L. Kouznetsova 1,2, Santosh Kesari 3 and Igor F. Tsigelny 1,2,4, *  \nCitation: Li, S.; Kouznetsova, V.L.;  \nKesari, S.; Tsigelny, I.F. piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer. Molecules 2024, 29, 4311. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)molecules29184311  \nAcademic Editors: Vladimir N. Uversky and Andreas Tsakalof  \nReceived: 2 July 2024  \nRevised: 29 August 2024  \nAccepted: 6 September 2024  \nPublished: 11 September 2024  \nCopyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 CureScience Institute, San Diego, CA 92121, USA; [applerx@gmail.com](applerx@gmail.com) (S.L.); [vkouznetsova@ucsd.edu](vkouznetsova@ucsd.edu) (V.L.K.)  \n2 San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA  \n3 Pacific Neuroscience Institute, Santa Monica, CA 90404, USA; santosh.kesari@providence.org  \n4 Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA  \n* Correspondence: [igor@curescience.org](igor@curescience.org)  \nAbstract: Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs—piwi-RNAs (piRNAs)—and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC) . Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers. With this data, we developed a machine learning (ML) algorithm and created 1020 different piRNA sequence descriptors. With the Naïve Bayes Multinomialclassifier, we were able to isolate the 27 most influential sequence descriptors and achieve an accuracy of 96.4% . To test the validity of our model, we used data from piRBase with known associations with CRC that we did not use to train the ML model. We were able to achieve an accuracy of 85.7% with these new independent data. To further validate our model, we also tested data from unrelated diseases, including piRNAs with a correlation to breast cancer and no proven correlation to CRC. The model scored 44.4% on these piRNAs, showing that it can identify a difference between biomarkers of CRC and biomarkers of other diseases. The final results show that our model is an effective tool for diagnosing colorectal cancer. We believe that in the future, this model will prove useful for colorectal cancer and other diseases diagnostics.  \nKeywords: piRNA; machine learning; colorectal cancer; diagnostics  \n1. Introduction  \nPiwi-interacting RNAs, also known as piRNAs, are RNAs with 24–31 nucleotides found in the germline of many species. They are the largest class of non-coding RNAs (functional RNAs that are not translated into a protein) . Studies have shown the role of piRNAs as biomarkers and therapeutic targets for cancer patients [1] . An example of this is piR-36712, whose concentration is negatively correlated with tumor sizes within breast cancer [2] . The functions of piRNAs ar","cbCaiaJ1ii5fxtKQ","https://ap.wps.com/l/cbCaiaJ1ii5fxtKQ","pdf",1983697,3,1,11,"English","en",105,"# Introduction\n## piRNAs as biomarkers and therapeutic targets\n## CRC importance and early detection\n## Prior research on piRNAs and CRC\n## Study rationale and machine-learning approach","[{\"question\":\"What is the main goal of this research on piRNAs and colorectal cancer?\",\"answer\":\"The study aims to determine whether piRNAs can be used as objective biomarkers for diagnosing colorectal cancer using machine learning.\"},{\"question\":\"How were candidate piRNAs selected for model training?\",\"answer\":\"Thirteen differently expressed piRNAs were selected from previously published serum samples of CRC patients.\"},{\"question\":\"How was the model validated beyond the training dataset?\",\"answer\":\"Validation used independent associations from piRBase that were not included in training, and additional tests were performed on piRNAs correlated with breast cancer and unrelated to CRC.\"}]","piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer | PDF",1785902205,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"pirna-in-machine-learning-based-diagnostics-of-colorectal-cancer","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/pirna-in-machine-learning-based-diagnostics-of-colorectal-cancer/125952/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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 is the main goal of this research on piRNAs and colorectal cancer?","Question",{"text":76,"@type":77},"The study aims to determine whether piRNAs can be used as objective biomarkers for diagnosing colorectal cancer using machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were candidate piRNAs selected for model training?",{"text":81,"@type":77},"Thirteen differently expressed piRNAs were selected from previously published serum samples of CRC patients.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the model validated beyond the training dataset?",{"text":85,"@type":77},"Validation used independent associations from piRBase that were not included in training, and additional tests were performed on piRNAs correlated with breast cancer and unrelated to CRC.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]