[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120854-en":3,"doc-seo-120854-105":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},120854,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Digital High Resolution Melt and Machine Learning for the Classification and Novelty Detection of Fungal Pathogens","The thesis develops and evaluates a machine-learning workflow integrated with Digital High Resolution Melt (dHRM) to classify and detect novelty in fungal pathogens. It examines preprocessing choices through signal filtering, and assesses how feature extraction and selection transformations influence classifier performance on dHRM curves. Experiments include different ramping rates and remelt strategies, followed by novelty detection supported by a micromanipulator design and prepared dHRM assay setups.","UC San Diego  \nUC San Diego Electronic Theses and Dissertations  \nTitle  \nDigital High Resolution Melt and Machine Learning for the Classification and Novelty Detection of Fungal Pathogens  \nPermalink  \n[https://escholarship.org/uc/item/89r0x72k](https://escholarship.org/uc/item/89r0x72k)  \nAuthor  \nSun, Haoxiang  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA SAN DIEGO  \nDigital High Resolution Melt and Machine Learning for the Classification and Novelty Detection  \nof Fungal Pathogens  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Science  \nin  \nBioengineering  \nby  \nHaoxiang Sun  \nCommittee in charge:  \nProfessor Stephanie Fraley, Chair  \nProfessor Jeff Hasty  \nProfessor Shamim Nemati  \nCopyright Haoxiang Sun 2023 All rights reserved.  \nThe thesis of Haoxiang Sun is approved, and it is acceptable in quality and form for publication on microfilm and electronically.  \nUniversity of California San Diego  \n2023  \nDEDICATION  \nTo thank my parents, friends, principal investigator Dr. Stephanie Fraley and mentors Tyler Goshia & April Aralar for the support.  \nTABLE OF CONTENTS  \nTHESIS APPROVAL PAGE..........................................................................................................iii  \nDEDICATION................................................................................................................................iv  \nTABLE OF CONTENTS.................................................................................................................v  \nLIST OF FIGURES.......................................................................................................................vii  \nLIST OF TABLES........................................................................................................................viii  \nACKNOWLEDGEMENTS............................................................................................................ix  \nABSTRACT OF THESIS................................................................................................................x  \nChapter 1: Introduction.................................................................................................................... 1  \n1.1 Importance of Fungal Pathogens................................................................................... 1  \n1.2 Aspergillus.....................................................................................................................3  \n1.3 Digital High Resolution Melt (dHRM)..........................................................................4  \n1.4 Machine Learning..........................................................................................................8  \n1.4.1 Applicability of Machine Learning Classifier in dHRM Curve Analysis......8  \n1.4.2 Random Forest & Feature Selection...............................................................9  \n1.5 Signal Filtering for Melt Curves.................................................................................. 10  \nChapter 2: Machine Learning........................................................................................................ 12  \n2.1 Introduction.................................................................................................................. 12  \n2.2 Methods & Materials................................................................................................... 13  \n2.2.1 Basic Specifications of Machine Learning & Signal Preprocessing............13  \n2.2.2 Filters for Signal Preprocessing Assessment................................................14  \n2.2.3 Feature Extraction & Selection Transformations Assessment......................15  \n2.3 Results...............................................................................................................","cbCaieVNpO0NpwNA","https://ap.wps.com/l/cbCaieVNpO0NpwNA","pdf",9654472,1,66,"English","en",105,"# Chapter 1: Introduction\n## Importance of Fungal Pathogens\n## Aspergillus\n## Digital High Resolution Melt (dHRM)\n## Machine Learning\n## Signal Filtering for Melt Curves\n# Chapter 2: Machine Learning\n## Introduction\n## Methods & Materials\n## Results\n# Chapter 3: Different Ramping Rates\n## Introduction\n## Methods & Materials\n## Results\n# Chapter 4: Micromanipulator for Novelty Detection\n## Introduction\n## Methods & Materials\n## Results\n# Chapter 5: Conclusion & Discussion\n## Machine Learning\n## Different Melt Rates","[{\"question\":\"What is the main purpose of the thesis?\",\"answer\":\"To integrate machine learning with Digital High Resolution Melt (dHRM) for classification and novelty detection of fungal pathogens.\"},{\"question\":\"How does the thesis improve model performance using signal processing?\",\"answer\":\"It studies signal filtering for melt curves and evaluates how different filters affect machine-learning results on dHRM data.\"},{\"question\":\"What additional experiments support novelty detection beyond basic classification?\",\"answer\":\"The work explores different ramping rates with remelt raw results and develops a micromanipulator approach for novelty detection using prepared dHRM designs.\"}]","Digital High Resolution Melt and Machine Learning for the Classification and Novelty Detection of Fungal Pathogens | PDF",1785732365,166,{"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},"digital-high-resolution-melt-and-machine-learning-for-the-classification-and-novelty-detection-of-fungal-pathogens","",{"@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/digital-high-resolution-melt-and-machine-learning-for-the-classification-and-novelty-detection-of-fungal-pathogens/120854/",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 purpose of the thesis?","Question",{"text":75,"@type":76},"To integrate machine learning with Digital High Resolution Melt (dHRM) for classification and novelty detection of fungal pathogens.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis improve model performance using signal processing?",{"text":80,"@type":76},"It studies signal filtering for melt curves and evaluates how different filters affect machine-learning results on dHRM data.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional experiments support novelty detection beyond basic classification?",{"text":84,"@type":76},"The work explores different ramping rates with remelt raw results and develops a micromanipulator approach for novelty detection using prepared dHRM designs.","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"]