[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291199-105":59,"doc-detail-291199-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","a-machine-learning-and-directed-network-optimization-approach-to-uncover-tp53-regulatory-patterns","A machine learning and directed network optimization approach to uncover - TP53 regulatory patterns","","TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers, and understanding how its regulation changes is crucial. The study combines machine learning to predict TP53 mutation status from transcriptomic regulon expression and directed network optimization to reconstruct how specific mutations alter TP53 target transcript levels. Using CCLE and TCGA data, it infers regulatory profiles across multiple mutation types, hotspots, protein changes, and stress conditions such as irradiation and hypoxia, revealing condition- and mutation-dependent differences that support downstream functional characterization and therapeutic design.",{"@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/a-machine-learning-and-directed-network-optimization-approach-to-uncover-tp53-regulatory-patterns/291199/",{"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/a-machine-learning-and-directed-network-optimization-approach-to-uncover-tp53-regulatory-patterns/291199.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study use machine learning to analyze TP53?","Question",{"text":112,"@type":113},"Machine learning predicts TP53 mutation status from transcriptomic data based on target gene expression summarized as regulons.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What role does directed network optimization play?",{"text":117,"@type":113},"Directed network optimization infers TP53 regulatory profiles and reconstructs how mutations affect TP53 target transcript levels across different mutation types and stress conditions.",{"name":119,"@type":110,"acceptedAnswer":120},"What factors influence the inferred TP53 regulatory differences?",{"text":121,"@type":113},"The regulatory profiles vary with mutation type, deleterious consequences, known hotspots, protein changes, and stress conditions such as irradiation and hypoxia.","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},291199,1790192910,{"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":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","iScience  \nll  \nOPEN ACCESS  \nArticle  \nA machine learning and directed network optimization approach to uncover TP53 regulatory patterns  \nCharalampos P. Triantafyllidis, Alessandro Barberis, Fiona Hartley, ..., Linda van Bijsterveldt, Julio Saez Rodriguez, Francesca M. Buffa  \n[c.triantafyllidis@imperial.ac.uk](c.triantafyllidis@imperial.ac.uk)[ ](c.triantafyllidis@imperial.ac.uk)(C.P.T.)  \nfrancesca. buffa@oncology. ox. [ac.uk](ac.uk) (F. M. B.)  \nHighlights  \nMachine learning predicts TP53 mutation status using mRNA expression of target genes (regulon)  \nTP53 regulatory activity inferred for different mutations in CCLE and TCGA using directed causal networks  \nSimilar regulatory networks are observed when same mutation, deleterious function, and hotspot  \nSame stress TP53  \nperturbation (hypoxia/ irradiation) results in similar regulatory activity  \nTriantafyllidis et al., iScience 26, 108291  \nDecember 15, 2023 ª 2023 The Author(s) .  \n[https://doi.org/10.1016/](https://doi.org/10.1016/)[ ](https://doi.org/10.1016/)[j.isci.2023.108291](j.isci.2023.108291)  \niScience  \nll  \nOPEN ACCESS  \nArticle  \nA machine learning and directed network optimization approach to uncover  \nTP53 regulatory patterns  \nCharalampos P. Triantafyllidis, 1,2,* Alessandro Barberis, 1,3 Fiona Hartley, 1 Ana Miar Cuervo,1 Enio Gjerga,4 Philip Charlton, 1 Linda van Bijsterveldt,5 Julio Saez Rodriguez,4 and Francesca M. Buffa1,6,7,*  \nSUMMARY  \nTP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53 from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations,(2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type,(2) deleterious consequences of the mutation,(3) known hotspots,(4) protein changes,(5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.  \nINTRODUCTION  \nRegulation of gene expression is critical to a diverse array of biological processes in health and disease. Dynamic transcriptional changes drive cell fate decisions in development, disease, and in response to drugs and mitogens. Transcription factors (TFs) are master regulators of gene expression. Sequence-specific TFs can bind to exact regions of the DNA to facilitate transcription initiation of their target genes,1 known together as the regulon. In cancer, driver mutations have an impact onTFs and many of the key tumor-suppressor genes are TFs or cofactors.2 Such mutations lead to structural changes and alter the DNA-binding capacity of the respective TF, impacting their regulon. To prevent targeting cancer agents only to a limited set of molecules or mutations, and to broaden our possibilities for re-purposing of existing drugs, it is critical to understand these networks and to explore the full downstream functional implications of mutations.  \nGene networks are a powerful tool to study complex diseases such as cancer3–8; however, they have not been applied systematically to characterize different mutations. Different approaches can be chosen for gene networks analysis and inference, which provide different insights. For example, association networks have been used extensively, while their predictive ability ","cbCaif3hJmIYr4sj","https://ap.wps.com/l/cbCaif3hJmIYr4sj","pdf",7225039,"English","# Highlights\n## Machine learning prediction of TP53 mutation status\n## Directed network optimization for TP53 regulatory profiles\n## Consistent regulatory activity across mutation and stress contexts\n# Introduction\n## Gene regulation and regulon concept\n## Cancer driver mutations and transcription factor disruption\n## Limits of association networks and value of directed causal networks\n## TP53 function and stress-dependent outcomes","[{\"question\":\"How does the study use machine learning to analyze TP53?\",\"answer\":\"Machine learning predicts TP53 mutation status from transcriptomic data based on target gene expression summarized as regulons.\"},{\"question\":\"What role does directed network optimization play?\",\"answer\":\"Directed network optimization infers TP53 regulatory profiles and reconstructs how mutations affect TP53 target transcript levels across different mutation types and stress conditions.\"},{\"question\":\"What factors influence the inferred TP53 regulatory differences?\",\"answer\":\"The regulatory profiles vary with mutation type, deleterious consequences, known hotspots, protein changes, and stress conditions such as irradiation and hypoxia.\"}]","A machine learning and directed network optimization approach to uncover - TP53 regulatory patterns | PDF",1789643689,48]