[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120937-en":3,"doc-seo-120937-105":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},120937,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Baseline gene signatures of reactogenicity to Ebola vaccination - A machine learning approach across multiple cohorts","The study investigates how baseline gene expression relates to reactogenicity after Ebola vaccination, aiming to support personalized vaccinology and clarify molecular contributors to transient adverse events. Using prevaccination gene expression integrated with adverse events reported within 14 days post-vaccination, the research analyzes 144 genes across 343 blood samples from four phase I cohorts. A machine learning model identifies 22 key genes linked to local and systemic reactions, offering mechanistic insights into vaccine safety signals.","Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts  \nCarvalho, P.C.G.D.; Hirata, T.D.C.; Alves, L.Y.M.; Moscardini, I.F.; Nascimento, [A.P.B. do](A.P.B. do); Costa-Martins, A.G.;   ; VSV-EBOPLUS Consortia  \nCitation  \nCarvalho, P. C. G. D., Hirata, T. D. C., Alves, L. Y. M., Moscardini, I. F., Nascimento, A. P.  \n[B. do](B. do), Costa-Martins, A. G.,…Nakaya, H. I. (2023). Baseline gene signatures of  \nreactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts.  \nFrontiers In Immunology, 14. doi:10.3389/fimmu.2023.1259197  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/3713794](https://hdl.handle.net/1887/3713794)  \nNote: To cite this publication please use the final published version (if applicable) .  \nTYPE Original Research PUBLISHED 08 November 2023 DOI 10.3389/fimmu.2023.1259197  \nOPEN ACCESS  \nEDITED BY  \nFrancesco Pappalardo, University of Catania, Italy  \nREVIEWED BY  \nElke Bergmann-Leitner,  \nWalter Reed Army Institute of Research, United States  \nSaranya Sridhar,  \nSanofi Pasteur, United Kingdom  \n*CORRESPONDENCE Helder I. Nakaya  \n [helder.nakaya@einstein.br](helder.nakaya@einstein.br)  \nRECEIVED 15 July 2023  \nACCEPTED 23 October 2023  \nPUBLISHED 08 November 2023  \nCITATION  \nGonzalez Dias Carvalho PC, Dominguez Crespo Hirata T,  \nMano Alves LY, Moscardini IF, do Nascimento APB, Costa-Martins AG, Sorgi S, Harandi AM, Ferreira DM, Vianello E, Haks MC, Ottenhoff THM, Santoro F, Martinez-Murillo P, Huttner A, Siegrist C-A, Medaglini D and Nakaya HI (2023) Baseline gene signatures of  \nreactogenicity to Ebola vaccination: a machine learning  \napproach across multiple cohorts.  \nFront. Immunol. 14:1259197 .  \ndoi: 10.3389/fimmu.2023.1259197  \nCOPYRIGHT  \n© 2023 Gonzalez Dias Carvalho, Dominguez Crespo Hirata, Mano Alves, Moscardini, do Nascimento, Costa-Martins, Sorgi, Harandi, Ferreira, Vianello, Haks, Ottenhoff, Santoro, Martinez-Murillo, Huttner, Siegrist, Medaglini and Nakaya. This is an open-access 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.  \nBaseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts  \nPatrícia Conceição Gonzalez Dias Carvalho 1,2  \n,  \nThiago Dominguez Crespo Hirata 3,  \nLeandro Yukio Mano Alves 3, Isabelle Franco Moscardini 4, Ana Paula Barbosa do Nascimento 5,  \nAndr´e G. Costa-Martins 3,6, Sara Sorgi 7, Ali M. Harandi 8,9, Daniela M. Ferreira 1,2, Eleonora Vianello 10, Mariëlle C. Haks 10, Tom H. M. Ottenhoff 10, Francesco Santoro 7,  \nPaola Martinez-Murillo 11, for VSV-EBOVAC Consortia, for VSV-EBOPLUS Consortia, Angela Huttner 11,12, Claire-Anne Siegrist 11, Donata Medaglini 13 and Helder I. Nakaya 14,15*  \n1Oxford Vaccine Group, University of Oxford, Oxford, United Kingdom, 2 Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom, 3 Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo,  \nSão Paulo, Brazil, 4 Microbiotec Srl, Siena, Italy, 5 Division of Infectious Diseases, Cincinnati Children ’s Hospital Medical Center, Cincinnati, OH, United States, 6Artificial Intelligence and Analytics Department, Institute for Technological Research, São Paulo, Brazil, 7 Laboratory of Molecular Microbiology and Biotechnology (LAMMB), Department of Medical Biotechnologies, University of Siena, Siena, Italy, 8 Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska Academy, Univers","cbCaitQerwQFQUPU","https://ap.wps.com/l/cbCaitQerwQFQUPU","pdf",2928352,1,11,"English","en",105,"# Introduction\n## Ebola vaccine reactogenicity and rationale\n# Methods\n## Machine learning integration of gene expression and adverse events\n# Results and Discussion\n## Gene analysis across multiple cohorts\n## Key genes linked to adverse reactions","[{\"question\":\"What question does the study address about Ebola vaccination reactogenicity?\",\"answer\":\"It examines whether baseline gene signatures can predict reactogenicity and help explain molecular factors behind transient adverse reactions after vaccination.\"},{\"question\":\"How were baseline gene expression data and adverse events combined in the analysis?\",\"answer\":\"The approach integrates prevaccination gene expression with adverse events occurring within 14 days post-vaccination, then applies a machine learning model to link patterns to outcomes.\"},{\"question\":\"What main findings link genes to adverse events after vaccination?\",\"answer\":\"Across 343 blood samples and 144 genes, the model identifies 22 key genes associated with both local and systemic adverse reactions such as fatigue, headache, fever, and arthritis.\"}]","Baseline gene signatures of reactogenicity to Ebola vaccination - A machine learning approach across multiple cohorts | PDF",1785732855,28,{"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},"baseline-gene-signatures-of-reactogenicity-to-ebola-vaccination-a-machine-learning-approach-across-multiple-cohorts","",{"@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/baseline-gene-signatures-of-reactogenicity-to-ebola-vaccination-a-machine-learning-approach-across-multiple-cohorts/120937/",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","2026-08-03",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 question does the study address about Ebola vaccination reactogenicity?","Question",{"text":75,"@type":76},"It examines whether baseline gene signatures can predict reactogenicity and help explain molecular factors behind transient adverse reactions after vaccination.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were baseline gene expression data and adverse events combined in the analysis?",{"text":80,"@type":76},"The approach integrates prevaccination gene expression with adverse events occurring within 14 days post-vaccination, then applies a machine learning model to link patterns to outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"What main findings link genes to adverse events after vaccination?",{"text":84,"@type":76},"Across 343 blood samples and 144 genes, the model identifies 22 key genes associated with both local and systemic adverse reactions such as fatigue, headache, fever, and arthritis.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]