[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119968-en":3,"doc-seo-119968-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119968,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Leveraging Infrared Imaging with Machine Learning for Phenotypic Profiling - Doctor of Philosophy thesis","Phenotypic profiling maps observable traits of cells, tissues, organisms, and systems under chemical, genetic, and disease perturbations to reveal functional consequences for biological research. Prior imaging and omics-based approaches quantify morphology, metabolism, and gene expression but face cost, operational complexity, and strong batch effects. Infrared (IR) imaging provides high-throughput, fast full-spectrum capture with label-free biochemical fingerprints. This thesis develops IR-based probe design, multi-level phenotypic profiling workflows, and end-to-end analysis pipelines, including preprocessing, statistics, and machine learning, with algorithmic advances to measure and map phenotypes.","Leveraging Infrared Imaging with Machine Learning for Phenotypic Profiling  \nXinwen Liu  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences  \nCOLUMBIA UNIVERSITY  \n2024  \n© 2024  \nXinwen Liu  \nAll Rights Reserved  \nAbstract  \nLeveraging Infrared Imaging with Machine Learning  \nfor Phenotypic Profiling  \nXinwen Liu  \nPhenotypic profiling systematically maps and analyzes observable traits (phenotypes) exhibited in cells, tissues, organisms or systems in response to various conditions, including chemical, genetic and disease perturbations. This approach seeks to comprehensively understand the functional consequences of perturbations on biological systems, thereby informing diverse research areas such as drug discovery, disease modeling, functional genomics and systems biology. Corresponding techniques should capture high-dimensional features to distinguish phenotypes affected by different conditions. Current methods mainly include fluorescence imaging, mass spectrometry and omics technologies, coupled with computational analysis, to quantify diverse features such as morphology, metabolism and gene expression in response to perturbations. Yet, they face challenges of high costs, complicated operations and strong batch effects.  \nVibrational imaging offers an alternative for phenotypic profiling, providing a sensitive, cost-effective and easily operated approach to capture the biochemical fingerprint of phenotypes. Among vibrational imaging techniques, infrared (IR) imaging has further advantages of high throughput, fast imaging speed and full spectrum coverage compared with Raman imaging. However, current biomedical applications of IR imaging mainly concentrate on \"digital disease pathology\", which uses label-free IR imaging with machine learning for tissue pathology classification and disease diagnosis.  \nThe thesis contributes as the first comprehensive study of using IR imaging for phenotypic profiling, focusing on three key areas. First, IR-active vibrational probes are systematically  \ndesigned to enhance metabolic specificity, thereby enriching measured features and improving sensitivity and specificity for phenotype discrimination. Second, experimental workflows are established for phenotypic profiling using IR imaging across biological samples at various levels, including cellular, tissue and organ, in response to drug and disease perturbations. Lastly, complete data analysis pipelines are developed, including data preprocessing, statistical analysis and machine learning methods, with additional algorithmic developments for analyzing and mapping phenotypes.  \nChapter 1 lays the groundwork for IR imaging by delving into the theory of IR spectroscopy theory and the instrumentation of IR imaging, establishing a foundation for subsequent studies.  \nChapter 2 discusses the principles of popular machine learning methods applied in IR imaging, including supervised learning, unsupervised learning and deep learning, providing the algorithmic backbone for later chapters. Additionally, it provides an overview of existing biomedical applications using label-free IR imaging combined with machine learning, facilitating a deeper understanding of the current research landscape and the focal points of IR imaging for traditional biomedical studies.  \nChapter 3-5 focus on applying IR imaging coupled with machine learning for novel application of phenotypic profiling. Chapter 3 explores the design and development of IR-active vibrational probes for IR imaging. Three types of vibrational probes, including azide, 13C-based probes and deuterium-based probes are introduced to study dynamic metabolic activities of protein, lipids and carbohydrates in cells, small organisms and mice for the first time. The developed probes largely improve the metabolic specificity of IR imaging, enhancing the sensitivity of IR imaging towards differen","cbCaiamVp8FNLNtF","https://ap.wps.com/l/cbCaiamVp8FNLNtF","pdf",22152307,1,180,"English","en",105,"# Chapter 1: Theory and instrumentation of infrared imaging\n# Chapter 2: Machine learning methods for IR imaging\n# Chapter 3: IR-active vibrational probes for phenotypic profiling\n# Chapter 4: IR imaging with labeling and unsupervised learning for tissue profiling\n# Chapter 5: VIBRANT for cellular phenotypic profiling of drug perturbations","[{\"question\":\"What is the purpose of phenotypic profiling in this thesis?\",\"answer\":\"It aims to systematically map observable phenotypes and quantify how biological systems respond to conditions such as chemical, genetic, and disease perturbations.\"},{\"question\":\"Why focus on infrared (IR) imaging instead of other imaging modalities?\",\"answer\":\"IR imaging is positioned as a sensitive, cost-effective, easy-to-operate approach with high throughput, fast imaging speed, and full-spectrum coverage, while enabling label-free biochemical fingerprinting.\"},{\"question\":\"What are the three main contributions of the thesis?\",\"answer\":\"It designs IR-active vibrational probes to enhance metabolic specificity, establishes IR imaging workflows across cellular, tissue, and organ samples, and develops complete data analysis pipelines with additional algorithmic methods for phenotype analysis and mapping.\"}]","Leveraging Infrared Imaging with Machine Learning for Phenotypic Profiling - Doctor of Philosophy thesis | PDF",1785727309,454,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"leveraging-infrared-imaging-with-machine-learning-for-phenotypic-profiling-doctor-of-philosophy-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/leveraging-infrared-imaging-with-machine-learning-for-phenotypic-profiling-doctor-of-philosophy-thesis/119968/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the purpose of phenotypic profiling in this thesis?","Question",{"text":76,"@type":77},"It aims to systematically map observable phenotypes and quantify how biological systems respond to conditions such as chemical, genetic, and disease perturbations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why focus on infrared (IR) imaging instead of other imaging modalities?",{"text":81,"@type":77},"IR imaging is positioned as a sensitive, cost-effective, easy-to-operate approach with high throughput, fast imaging speed, and full-spectrum coverage, while enabling label-free biochemical fingerprinting.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the three main contributions of the thesis?",{"text":85,"@type":77},"It designs IR-active vibrational probes to enhance metabolic specificity, establishes IR imaging workflows across cellular, tissue, and organ samples, and develops complete data analysis pipelines with additional algorithmic methods for phenotype analysis and mapping.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]