[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-356561-105":59,"doc-detail-356561-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","machine-learning-predicted-chromatin-organization-landscape-across-pediatric-tumors","Machine learning-predicted chromatin organization landscape across pediatric tumors","","Machine learning predicts how somatic structural variants disrupt 3D genome folding in pediatric cancers. Using the convolutional neural network Akita, the study models disruptions caused by SVs identified across 61 pediatric tumor types from the Children’s Brain Tumor Network dataset. Results show strong tumor-type variability and five recurrently disrupted regions enriched for high-impact events, including loci not necessarily highly mutated. Integrating epigenetic data, the authors apply a modified Activity-by-Contact approach to prioritize SVs overlapping active enhancers, highlighting effects near key oncogenes and proposing new candidate mechanisms.",{"@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/machine-learning-predicted-chromatin-organization-landscape-across-pediatric-tumors/356561/",{"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/machine-learning-predicted-chromatin-organization-landscape-across-pediatric-tumors/356561.png","ImageObject",300,407,{"name":92,"@type":93},"Jacob","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-27","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is experimental testing of structural variants on genome folding considered infeasible?","Question",{"text":112,"@type":113},"The approach would require measuring the effects of hundreds of thousands of SVs, which is costly, time-consuming, and impractical, especially for larger SVs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study predict SV disruptions in genome folding?",{"text":117,"@type":113},"It uses the Akita convolutional neural network to predict chromatin contact maps from DNA sequence, enabling in silico mutagenesis to quantify the isolated effect of each somatic SV.",{"name":119,"@type":110,"acceptedAnswer":120},"What does the modified Activity-by-Contact scoring contribute?",{"text":121,"@type":113},"By integrating epigenetic data, the method prioritizes SVs that disrupt genome contacts at active enhancers, helping identify disruptive variants near key oncogenes and novel candidate tumorigenic loci.","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},356561,1790393074,{"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":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962084931830,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning-predicted chromatin organization landscape across pediatric tumors  \nKetrin Gjoni1,2,7, Shu Zhang1,2,7, Rachel E. Yan3, Bo Zhang4, Daniel Miller4,  \nJeffrey P. Greenfield3, Adam Resnick4, Nadia Dahmane3 & Katherine S. Pollard1,2,5,6􀀍  \nStructural variants (SVs) are increasingly recognized as important contributors to oncogenesis through their effects on 3D genome folding. Recent advances in whole-genome sequencing have enabled large-scale profiling of SVs across diverse tumors, yet experimental characterization of their individual impact on genome folding remains infeasible. Here, we leveraged a convolutional neural network, Akita, to predict disruptions in genome folding caused by somatic SVs identified in 61 tumor types from the Children’s Brain Tumor Network dataset. Our analysis reveals significant variability in SVinduced disruptions across tumor types, with the most disruptive SVs coming from lymphomas and sarcomas, metastatic tumors, and germline cell tumors. Dimensionality reduction of disruption scores identified five recurrently disrupted regions enriched for high-impact SVs across multiple tumors. Some of these regions are highly disrupted despite not being highly mutated, and harbor tumor-associated genes and transcriptional regulators. To further interpret the functional relevance of high-scoring SVs, we integrated epigenetic data and developed a modified Activity-by-Contact scoring approach to prioritize SVs with disrupted genome contacts at active enhancers. This method highlighted highly disruptive SVs near key oncogenes, as well as novel candidate loci potentially implicated in tumorigenesis. These findings highlight the utility of machine learning for identifying novel SVs, loci, and genetic mechanisms contributing to pediatric cancers. This framework provides a foundation for future studies linking SV-driven regulatory changes to cancer pathogenesis.  \nPediatric brain tumors (PBTs) are the most common solid cancers in children and have seen little improvement in treatment or understanding compared to other childhood cancers1. One reason is that the genetic causes behind many PBTs remain unclear, and efforts are ongoing to dissect the underlying molecular mechanisms that promote tumorigenesis in these cancers. Structural variants (SVs)–large deletions, duplications, inversions, insertions, and chromosomal rearrangements of genomic regions–are major drivers of cancer that play an important role in shaping the transcriptomic landscape of PBTs2. SVs can disrupt tumor suppressors or amplify oncogenes by fusing genes, altering regulatory elements, or directly disrupting the genes themselves. In cancer, SVs have been shown to create harmful gene fusions such as BCR-ABL in leukemias and MYB-QKI rearrangements in pediatric gliomas3,4. Overall, PBTs have higher rates of SVs compared to other pediatric tumors5 and their SV patterns differ from those observed in adult tumors. Recent studies found both simple and complex SV signatures in PBTs, associated with differences in replication timing, GC content, and breakpoint proximity to repetitive elements, telomeres, and topologically associating domain (TAD) boundaries6.  \nMore recently, SVs have been suggested to cause cancer through alterations to genome organization7. In humans, the genome is folded into higher order structures, ranging from small loops of only a few kilobases (kb) in length to larger TADs that span multiple megabases (Mb) of DNA8. By measuring genome organization through conformation capture experiments, the effect of SVs on structures like TADs has been implicated in various PBTs. The deletion of a TAD boundary can merge two TADs and cause genes from one TAD to hijack enhancers from the previously distinct other TAD and become overexpressed. Enhancer hijacking has been shown to lead to the activation of oncogenes across cancer types9, 10, including pediatr","cbCaiflbUJcZwR31","https://ap.wps.com/l/cbCaiflbUJcZwR31","pdf",2887063,13,"English","# Introduction\n## Structural variants and pediatric brain tumor genomics\n## Genome organization and SV effects\n# Methods\n## Akita-based disruption prediction\n## In silico mutagenesis and scoring\n## Activity-by-Contact prioritization with epigenetic integration\n# Results\n## Tumor-type variability in SV-induced disruptions\n## Recurrently disrupted genomic regions\n## Prioritized SVs near oncogenes and candidate loci\n# Discussion\n## Implications for identifying novel SV mechanisms in pediatric cancers","[{\"question\":\"Why is experimental testing of structural variants on genome folding considered infeasible?\",\"answer\":\"The approach would require measuring the effects of hundreds of thousands of SVs, which is costly, time-consuming, and impractical, especially for larger SVs.\"},{\"question\":\"How does the study predict SV disruptions in genome folding?\",\"answer\":\"It uses the Akita convolutional neural network to predict chromatin contact maps from DNA sequence, enabling in silico mutagenesis to quantify the isolated effect of each somatic SV.\"},{\"question\":\"What does the modified Activity-by-Contact scoring contribute?\",\"answer\":\"By integrating epigenetic data, the method prioritizes SVs that disrupt genome contacts at active enhancers, helping identify disruptive variants near key oncogenes and novel candidate tumorigenic loci.\"}]","Machine learning-predicted chromatin organization landscape across pediatric tumors | PDF",1790125967,33]