[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124812-en":3,"doc-seo-124812-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},124812,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The mechanistic functional landscape of retinitis pigmentosa - a machine learning-driven approach to therapeutic target discovery","Retinitis pigmentosa is a leading genetic cause of blindness, yet effective treatments remain lacking. Mechanistic models, grounded in systems biology, help clarify how retinitis pigmentosa genes shape disease mechanisms. Combining mechanistic disease maps with drug–target interactions within machine learning enables identification of new therapeutic targets. The workflow supports drug repurposing by learning causal links between approved drug targets and functional circuits of the mechanistic disease model.","Esteban‑Medina et al.  \nJournal of Translational Medicine (2024) 22:139 [https://doi.org/10.1](https://doi.org/10.1) 186/s12967‑024‑04911‑7  \nJournal of Translational Medicine  \n RESEARCH Open Access  \nThe mechanistic functional landscape  \nof retinitis pigmentosa: a machine learning‑driven approach to therapeutic target discovery  \nMarina Esteban‑Medina 1,2† , Carlos Loucera 1,2† , Kinza Rian 1,2 , Sheyla Velasco3 ,  \nLorena Olivares‑González3 , Regina Rodrigo3,4,5,6,7 , Joaquin Dopazo 1,2,4* and Maria Peña‑Chilet 1,2,4,8*  \nAbstract  \nBackground Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effec‑ tive treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug‑target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel thera‑ peutic targets, facilitating further drug repurposing in RP.  \nMethods By mapping Retinitis Pigmentosa‑related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so‑defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi‑output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from Drug‑ Bank and the functional circuits ofthe mechanistic disease map. Selected target genes involvement were validatedon rd10 mice, a murine model of Retinitis Pigmentosa.  \nResults A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a poten‑ tial effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimen‑ tally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. Asa result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug‑disease interactions opens new avenues for boosting drug discovery. Such mechanistically‑ based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this  \n†Marina Esteban‑Medina and Carlos Loucera contributed equally to this work.  \n*Correspondence:  \nJoaquin Dopazo [joaquin.dopazo@juntadeandalucia.es](joaquin.dopazo@juntadeandalucia.es)[ ](joaquin.dopazo@juntadeandalucia.es)Maria Peña‑Chilet [maria_pena@iislafe.es](maria_pena@iislafe.es)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit","cbCaibGu5yNTHVym","https://ap.wps.com/l/cbCaibGu5yNTHVym","pdf",6734105,1,24,"English","en",105,"# Abstract\n# Background\n# Methods\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"What is the main goal of the study on retinitis pigmentosa?\",\"answer\":\"To construct a mechanistic functional map of retinitis pigmentosa and use machine learning to discover therapeutic target genes, enabling evaluation of approved drugs for potential repurposing.\"},{\"question\":\"How are retinitis pigmentosa genes connected to signaling pathways in the workflow?\",\"answer\":\"Retinitis pigmentosa–related genes from Orphanet, OMIM, and HPO are mapped onto KEGG signaling pathways to define signaling functional circuits that represent disease mechanisms.\"},{\"question\":\"Which model and validation steps are used to confirm predicted targets?\",\"answer\":\"An explainable multi-output random forest regressor is trained on normal tissue transcriptomic data to learn causal links, and selected targets are experimentally validated in rd10 mice, a murine model of retinitis pigmentosa.\"}]","The mechanistic functional landscape of retinitis pigmentosa - a machine learning-driven approach to therapeutic target discovery | PDF",1785894793,60,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-mechanistic-functional-landscape-of-retinitis-pigmentosa-a-machine-learning-driven-approach-to-therapeutic-target-discovery","",{"@graph":36,"@context":85},[37,54,68],{"@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/the-mechanistic-functional-landscape-of-retinitis-pigmentosa-a-machine-learning-driven-approach-to-therapeutic-target-discovery/124812/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study on retinitis pigmentosa?","Question",{"text":75,"@type":76},"To construct a mechanistic functional map of retinitis pigmentosa and use machine learning to discover therapeutic target genes, enabling evaluation of approved drugs for potential repurposing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are retinitis pigmentosa genes connected to signaling pathways in the workflow?",{"text":80,"@type":76},"Retinitis pigmentosa–related genes from Orphanet, OMIM, and HPO are mapped onto KEGG signaling pathways to define signaling functional circuits that represent disease mechanisms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model and validation steps are used to confirm predicted targets?",{"text":84,"@type":76},"An explainable multi-output random forest regressor is trained on normal tissue transcriptomic data to learn causal links, and selected targets are experimentally validated in rd10 mice, a murine model of retinitis pigmentosa.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]