[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128696-en":3,"doc-seo-128696-105":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},128696,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis - developing an individualized clinical prediction tool","Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by poor prognosis and non-specific clinical symptoms that complicate prediction of disease progression. This study develops molecular-level prognostic models to personalize treatment strategies for IPF using transcriptome sequencing and clinical data from 176 patients. A Random Survival Forest model demonstrates superior predictive accuracy and clinical utility via C-index, AUC, brief scores, and decision curve analysis, with validation using an ableomycin-induced pulmonary fibrosis mouse model, alongside a novel prognostic staging system.","TYPE Original Research PUBLISHED 23 December 2024 DOI 10.3389/ebm.2024.10215  \nOPEN ACCESS  \n*CORRESPONDENCE  \nChunnian Ren,  \n [chunnian0328@foxmail.com](chunnian0328@foxmail.com)  \nRECEIVED 26 April 2024  \nACCEPTED 11 December 2024  \nPUBLISHED 23 December 2024  \nCITATION  \nRuan H and Ren C (2024) Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool. Exp. Biol. Med. 249:10215 .  \ndoi: 10.3389/ebm.2024.10215  \nCOPYRIGHT  \n© 2024 Ruan and Ren. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIntegrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool  \nHongmei Ruan 1 and Chunnian Ren 2*  \n1Department of Pediatric Neurology, Chengdu Women’s and Children ’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China, 2Department of Pediatric Surgery, Chengdu Women ’s and Children ’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China  \nAbstract  \nIdiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic models to personalize treatment strategies for IPF patients. Using transcriptome sequencing and clinical data from 176 IPF patients, we developed a Random Survival Forest (RSF) model through machine learning and bioinformatics techniques. The model demonstrated superior predictive accuracy and clinical utility, as shown by the concordance index (C-index), the area under the operating characteristic curve (AUC), Brief scores, and decision curve analysis (DCA) curves. Additionally, a novel prognostic staging system was introduced to stratify IPF patients into distinct risk groups, enabling individualized predictions. The model’s performance was validated using ableomycin-induced pulmonary fibrosis mouse model. In conclusion, this study offers a new prognostic staging system and predictive tool for IPF, providing valuable insights for treatment and management.  \nKEYWORDS  \nidiopathic pulmonary fibrosis, machine learning, prediction model, random survival forest, hub gene  \nImpact statement  \nThe lack of specificity of the clinical symptoms ofIPF makes it difficult to predict the prognosis of IPF patients by clinical symptoms, and the establishment of a prediction model by identifying prognostic genes has become another possible method to determine the prognosis of IPF patients. To establish a prediction model with higher predictive  \nExperimental Biology and Medicine  \nPublished by Frontiers  \n01 Society for Experimental Biology and Medicine  \nperformance, we compared the predictive performance of the conventional model and machine learning model, identified a prediction model with the best predictive performance, and developed it into a prediction tool. The current study provides a new tool for individualized treatment of IPF.  \nIntroduction  \nIPF is a chronic, progressive interstitial lung disease defined by fibrosis, inflammation, and lung structure destruction [1, 2] . Predominantly affecting the elderly and middle-aged, IPF carries a poor prognosis [3], with a median survival time of 2–4 years post-diagnosis [3] . The current therapeutic mainstays, pirfenidone and nintedanib, offer only symptomatic relief by slowing the fibrotic progression [4, 5] . I","cbCaierf6ulyt51K","https://ap.wps.com/l/cbCaierf6ulyt51K","pdf",3520457,1,13,"English","en",105,"# Abstract\n# Introduction\n## Clinical challenge in predicting IPF progression\n## Machine learning prediction models and gaps\n## Rationale for molecular-level prognostic tools","[{\"question\":\"What problem does this study address in idiopathic pulmonary fibrosis (IPF)?\",\"answer\":\"Non-specific IPF symptoms make prognosis and disease progression difficult to predict reliably using clinical features alone.\"},{\"question\":\"What modeling approach is used to predict IPF prognosis?\",\"answer\":\"The study uses a Random Survival Forest (RSF) model built from transcriptome sequencing and clinical data, aiming for improved predictive accuracy.\"},{\"question\":\"How is the model’s usefulness evaluated and validated?\",\"answer\":\"Predictive performance is assessed using metrics such as C-index and AUC, with decision curve analysis, and the model is validated using an ableomycin-induced pulmonary fibrosis mouse model.\"}]","Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis - developing an individualized clinical prediction tool | PDF",1786002714,33,{"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},"integrating-machine-learning-with-bioinformatics-for-predicting-idiopathic-pulmonary-fibrosis-prognosis-developing-an-individualized-clinical-prediction-tool","",{"@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/integrating-machine-learning-with-bioinformatics-for-predicting-idiopathic-pulmonary-fibrosis-prognosis-developing-an-individualized-clinical-prediction-tool/128696/",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","2026-08-25","2026-08-06",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 problem does this study address in idiopathic pulmonary fibrosis (IPF)?","Question",{"text":76,"@type":77},"Non-specific IPF symptoms make prognosis and disease progression difficult to predict reliably using clinical features alone.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What modeling approach is used to predict IPF prognosis?",{"text":81,"@type":77},"The study uses a Random Survival Forest (RSF) model built from transcriptome sequencing and clinical data, aiming for improved predictive accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the model’s usefulness evaluated and validated?",{"text":85,"@type":77},"Predictive performance is assessed using metrics such as C-index and AUC, with decision curve analysis, and the model is validated using an ableomycin-induced pulmonary fibrosis mouse model.","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":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]