[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125071-en":3,"doc-seo-125071-105":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},125071,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","CORAL SCAR INVESTIGATION - AN APPLICATION OF MACHINE LEARNING AND COMPUTATIONAL BIOLOGY METHODS TO UNDERSTAND CORAL HOLOBIONT RESPONSE TO VARIOUS TISSUE LOSS DISEASES - Dissertation Abstract","Coral disease threatens reef biodiversity by driving coral decline, making immune-linked biomarkers essential for effective restoration. This dissertation applies machine learning and computational biology to two previously published tissue-loss disease exposure datasets to find genes tied to coral immune pathways, disease susceptibility, and classification among tissue loss diseases. Comparative analyses identify a melanin cascade and key melanin-synthesis enzyme as candidate constitutive immune traits. Differential expression and machine learning reveal 463 host-gene biomarkers for distinguishing diseases, while algal endosymbiont expression yields 407 disease-classification biomarkers. Results support using computational models to derive immune pathway patterns reflecting how the coral holobiont responds to different diseases.","University of Texas at Arlington  \nMavMatrix  \n\n| Biology Dissertations | Department of Biology |\n| --- | --- |\n| Fall 2024\u003Cbr>CORAL SCAR INVESTIGATION: AN APPLICATION OF MACHINE LEARNING AND COMPUTATIONAL BIOLOGY METHODS TO UNDERSTAND CORAL HOLOBIONT RESPONSE TO VARIOUS TISSUE LOSS DISEASES\u003Cbr>Emily W. Van Buren\u003Cbr>University of Texas at Arlington\u003Cbr>Follow this and additional works at: [https://mavmatrix.uta.edu/biology_dissertations](https://mavmatrix.uta.edu/biology_dissertations)\u003Cbr> Part of the Bioinformatics Commons, Biostatistics Commons, Computational Biology Commons, Immunity Commons, Marine Biology Commons, Molecular Genetics Commons, Multivariate Analysis Commons, and the Probability Commons |  |\n\nRecommended Citation  \nVan Buren, Emily W., \"CORAL SCAR INVESTIGATION: AN APPLICATION OF MACHINE LEARNING AND COMPUTATIONAL BIOLOGY METHODS TO UNDERSTAND CORAL HOLOBIONT RESPONSE TO VARIOUS TISSUE LOSS DISEASES\" (2024) . Biology Dissertations. 224.  \n[https://mavmatrix.uta.edu/biology_dissertations/224](https://mavmatrix.uta.edu/biology_dissertations/224)  \nThis Dissertation is brought to you for free and open access by the Department of Biology at MavMatrix. It has been accepted for inclusion in Biology Dissertations by an authorized administrator of MavMatrix.  \nCORAL SCAR INVESTIGATION: AN APPLICATION OF MACHINE LEARNING AND COMPUTATIONAL BIOLOGY METHODS TO UNDERSTAND CORAL HOLOBIONT RESPONSE TO VARIOUS TISSUE LOSS DISEASES  \nby  \nEMILY WYNNE VAN BUREN  \nDISSERTATION  \nSubmitted in partial fulfillment of the requirements  \nfor the degree of Doctor of Philosophy at  \nThe University of Texas at Arlington  \nDecember 2024  \nArlington, Texas  \nSupervising Committee:  \nLaura D Mydlarz (Supervising Professor) Todd Castoe  \nJeffery P Demuth Alison Ravencraft Li Wang  \nAbstract  \nCoral Scar Investigation: An Application of Machine Learning and Computational Biology Methods to Understand Coral Holobiont Response to Various Tissue Loss Diseases  \nEmily Wynne Van Buren, Ph.D  \nThe University of Texas at Arlington, 2024  \nSupervising Professor: Laura D Mydlarz  \nCoral disease is one of the biggest challenges facing coral reefs that actively changes biodiversity resulting in coral decline. With the rising threat of diseases, corals require biomarkers that reflect the immune systems and differences between common coral tissue loss diseases to best assist in coral restoration efforts. To obtain these biomarkers, my dissertation leverages two previously published datasets from two tissue loss disease exposure studies to investigate genes that are relevant for coral immune pathways, disease susceptibility, and classification between the diseases. In Chapter 2, I use comparative computational biology tools and protein assays to identify the melanin cascade in stony corals and the primary enzyme responsible for melanin synthesis. Melanin was identified to be a potential constitutive immune trait for corals affected by stony coral tissue loss disease. In Chapter 3, I apply differential expression and machine learning techniques to characterize and classify two tissue loss diseases based on coral host gene expression with a total of 463 biomarkers identified to characterize the two diseases. Finally, in Chapter 4, I apply machine learning methods to characterize and classify tissue loss diseases based on the algal endosymbiont gene expression, where 407 biomarkers were identified for disease classification. Overall, these chapters support the hypothesis that the application of machine learning and computational biology approaches applied to existing coral disease datasets will yield disease classification biomarkers that  \ndetermine immune patterns or pathways indicative of how the coral holobiont respond to  \ndifferent diseases.  \nCopyright by  \nEmily Wynne Van Buren  \n2024  \nChapter 2 is published under an open-access license in Integrative Comparative Biology.  \nDOI: [https://doi.org/10.1093/icb/icae115](https://doi.org/10.1093/icb/icae1","cbCaigVFfXr0SDrx","https://ap.wps.com/l/cbCaigVFfXr0SDrx","pdf",10171121,1,183,"English","en",105,"# Abstract\n## Chapter 2: Melanin cascade identification\n## Chapter 3: Differential expression and machine learning for host genes\n## Chapter 4: Machine learning for algal endosymbiont gene expression\n## Overall findings and implications","[{\"question\":\"What problem does the dissertation address in coral reef health?\",\"answer\":\"Coral diseases reduce reef biodiversity by causing coral decline, creating a need for immune-relevant biomarkers that distinguish tissue loss disease types to support restoration.\"},{\"question\":\"How does the dissertation generate biomarkers for disease understanding?\",\"answer\":\"It leverages two previously published tissue-loss exposure datasets and uses comparative computational biology, differential expression analysis, and machine learning to identify genes and biomarkers linked to immune pathways and disease classification.\"},{\"question\":\"What biomarkers and gene sources are used to distinguish tissue loss diseases?\",\"answer\":\"Host gene expression analysis identifies 463 biomarkers for classification of two tissue loss diseases, and algal endosymbiont gene expression identifies 407 biomarkers for disease classification.\"}]","CORAL SCAR INVESTIGATION - AN APPLICATION OF MACHINE LEARNING AND COMPUTATIONAL BIOLOGY METHODS TO UNDERSTAND CORAL HOLOBIONT RESPONSE TO VARIOUS TISSUE LOSS DISEASES - Dissertation Abstract | PDF",1785896475,461,{"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},"coral-scar-investigation-an-application-of-machine-learning-and-computational-biology-methods-to-understand-coral-holobiont-response-to-various-tissue-loss-diseases-dissertation-abstract","",{"@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/coral-scar-investigation-an-application-of-machine-learning-and-computational-biology-methods-to-understand-coral-holobiont-response-to-various-tissue-loss-diseases-dissertation-abstract/125071/",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 problem does the dissertation address in coral reef health?","Question",{"text":75,"@type":76},"Coral diseases reduce reef biodiversity by causing coral decline, creating a need for immune-relevant biomarkers that distinguish tissue loss disease types to support restoration.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation generate biomarkers for disease understanding?",{"text":80,"@type":76},"It leverages two previously published tissue-loss exposure datasets and uses comparative computational biology, differential expression analysis, and machine learning to identify genes and biomarkers linked to immune pathways and disease classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What biomarkers and gene sources are used to distinguish tissue loss diseases?",{"text":84,"@type":76},"Host gene expression analysis identifies 463 biomarkers for classification of two tissue loss diseases, and algal endosymbiont gene expression identifies 407 biomarkers for disease classification.","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"]