[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119838-en":3,"doc-seo-119838-105":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},119838,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Ageing-Associated Processes in Fission Yeast - An Integrated Machine Learning and Experimental Approach","This doctoral thesis integrates machine learning with experimental approaches to connect cellular pathways linked to ageing and to uncover ageing-associated processes in fission yeast. It introduces a novel proxy for chronological lifespan within a high-throughput chronological lifespan assay pipeline, enabling prediction of lifespan from simple traits and detection of ageing-associated phenotypes. The work further characterizes the transcription factor Hsr1, detailing direct regulatory targets, network links with other ageing-related transcription factors, and potential interaction mechanisms. The impact focuses on improving mechanistic understanding to support interventions for ageing-related health decline.","An Integrated Machine Learning and Experimental Approach to Uncover Ageing-Associated Processes in  \nFission Yeast  \nOlivia Valerie Hillson  \nSubmitted in partial fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nUniversity College London  \n2023  \nI, Olivia Valerie Hillson , confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.  \nAcknowledgements  \nI would like to acknowledge and thank Prof. Jürg Bähler and everyone at the Bählerlab (past and present) for their help and collaboration throughout this process and my second supervisor Prof. Christine Orengo and her lab for theirs.  \nExtra special thanks to María Rodríguez-López for holding my hand through everything I had to learn , to Shaimaa Hassan for being‘in this together’with me , and to Babis Rallis and Martina Neville , without whom I would never have even applied to do a PhD.  \nEternal gratitude to Jolanta Beinaroviča who forged through her own PhD one step ahead, dragging me along through mine in her wake, kicking and screaming. Thank you (I think?) for getting me here – you’re the best.  \nBig love to my family for always supporting me , no matter how crazy this journey seemed to you. And to Jack, thank you for going with me to the end, into the very fires of Mordor. Most importantly of all, thank you to my Mum , Producer of Doctors – everything I have and everything I am, I owe to you. This is no exception.  \nAnd finally, something I never imagined I would have to write. I would like to dedicate my thesis to the memory of StJohn Townsend. He was very special tous all, and I wouldn’t have finished this without his constant teaching, support , and friendship. He is sorely missed.  \n“When you are a Bear of Very Little Brain, and you Think of Things, you find sometimes that a Thing which seemed very Thingish inside you is quite different when it gets out into the open and has other people looking at it.”  \n-A.A. Milne, Winnie the Pooh  \nAbstract  \nThis work attempts to bring together knowledge of different pathways associated with cellular ageing and create connections between them using both machine learning and experimental methods. Initially, I describe the development of a novel proxy for chronological lifespan as part of the analysis pipeline of a high-throughput chronological lifespan assay in fission yeast. I then use this technique to go on to develop novel machine learning models that can predict lifespan, a complex phenotype, from simple traits, and identify ageingassociated phenotypes in fission yeast.  \nComplementary to this, I investigate a transcription factor of interest, Hsr1, for its involvement in cellular ageing and ageing-associated processes. I describe direct regulatory targets and how it forms a network with at least four other ageing-associated transcription factors which bridges the gaps between models of ageing, and suggest mechanisms for these interactions.  \nIn this way , this work provides novel links between cellular ageing mechanismsand ageing-associated processes from both machine learning and experimental sources.  \nImpact Statement  \nAgeing is one of the biggest socioeconomic challenges faced by the developed world today. Interventions for health decline in ageing populations and ageingrelated diseases, such as cancer and Alzheimer’s, can benefit from deeper understanding of the mechanisms which cause and control the ageing process. Current models of such mechanisms are widely disputed and generally thought to only tell part of the story. This project aims to develop machine learning approaches, integrated with experimental techniques, to give insights into the overarching mechanisms of the ageing process and attempt to elucidate how known pathways are linked.  \nThis thesis describes the application of high-throughput methods to determine the lifespan of natural strains of fission yeast for a machine lear","cbCaibBZmNXH2OkF","https://ap.wps.com/l/cbCaibBZmNXH2OkF","pdf",2438148,1,170,"English","en",105,"# Abstract\n# Impact Statement\n# Contents\n# Table of figures\n# Preface\n## Acknowledgements","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To integrate machine learning and experimental methods to uncover connections between cellular pathways associated with ageing and identify ageing-associated processes in fission yeast.\"},{\"question\":\"How does the thesis measure or predict chronological lifespan?\",\"answer\":\"It develops a novel proxy for chronological lifespan as part of a high-throughput assay pipeline, then builds machine learning models to predict lifespan from simple traits under multiple environmental conditions.\"},{\"question\":\"What role does the transcription factor Hsr1 play in the study?\",\"answer\":\"Hsr1 is investigated for involvement in cellular ageing, including resistance phenotypes under ageing-associated stresses and characterization of binding targets, motifs, and genome-wide genetic interactions.\"}]","Ageing-Associated Processes in Fission Yeast - An Integrated Machine Learning and Experimental Approach | PDF",1785726564,428,{"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},"ageing-associated-processes-in-fission-yeast-an-integrated-machine-learning-and-experimental-approach","",{"@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/ageing-associated-processes-in-fission-yeast-an-integrated-machine-learning-and-experimental-approach/119838/",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 is the main goal of the thesis?","Question",{"text":75,"@type":76},"To integrate machine learning and experimental methods to uncover connections between cellular pathways associated with ageing and identify ageing-associated processes in fission yeast.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis measure or predict chronological lifespan?",{"text":80,"@type":76},"It develops a novel proxy for chronological lifespan as part of a high-throughput assay pipeline, then builds machine learning models to predict lifespan from simple traits under multiple environmental conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does the transcription factor Hsr1 play in the study?",{"text":84,"@type":76},"Hsr1 is investigated for involvement in cellular ageing, including resistance phenotypes under ageing-associated stresses and characterization of binding targets, motifs, and genome-wide genetic interactions.","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"]