[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119160-en":3,"doc-seo-119160-105":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},119160,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","High-Dimensional Spectroscopy Analysis with Machine Learning Techniques - Thesis","High-dimensional spectroscopy delivers rich information, yet Raman signals in bio-samples are difficult to interpret because of their high dimensionality and complex measurement requirements. This thesis develops a platform combining graphene-assisted Raman spectroscopy with machine-learning interpretation to enable rapid screening of Alzheimer’s disease (AD) biomarkers in animal brains, supporting broader extension to tissues and biofluids. It also introduces ReflectoNet, a deep-learning-based computational reflectometry method that predicts complex refractive indices of thin films on nontrivial substrates from reflectance spectra, overcoming prior limitations in feasibility.","RICE UNIVERSITY  \nBy  \nA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE  \nAPPROVED, THESIS COMMITTEE  \nHOUSTON, TEXAS  \nABSTRACT  \nHigh-Dimensional Spectroscopy Analysis with Machine Learning  \nTechniques  \nby  \nZiyang Wang  \nHigh-dimensional spectroscopy often provides rich information. Raman spectroscopy is a non-destructive molecular sensing method. However, Raman signals in bio-samples are hard to interpret, due to the high dimensionality. Measurement of optical spectroscopy is also complicated and requires high-end instrumentations and intricate data analysis techniques. Machine learning methods offer great opportunities to extract subtle and deep information in high-dimensional spectra. They can also assist measurement of complex optical spectroscopy of materials with simpler optical setups. In this work, we develop a platform that enables rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in animal brains. The method facilitates the study of AD and can be extended to other tissues, biofluids, and for various other diseases. We also propose a computational reflectometry approach based on a deep learning model called ReflectoNet. It predicts complex refractive indices of thin films on top of nontrivial substrates from reflectance spectra, which was not feasible previously.  \nAcknowledgments  \nI would like to thank my advisor and chair of the committee, Prof. Shengxi Huang, and committee members, Prof. Genevera Allen and Prof. Ankit Patel for their valuable feedback and guidance. Additionally, I would like to thank Prof. Yuxuan Cosmi Lin and Prof. Xiaolei Sharon Huang for their contributions and advice while I was working on this research. I would like to express my gratitude toward my lab peers who were also very supportive. I also thank my family and friends for all their support and care during my pursuit of graduate studies.  \nThis thesis is based on our work Wang et al., which was accepted into the ACS nano [1], Copyright © 2022, American Chemical Society, and Wang et al., which was accepted into the 2D Materials [2] . The same texts and figures from these works were used in this thesis. Lastly, I would like to acknowledge the funding for the research works. I acknowledge the support from the National Institutes of Health under grant numbers R56AG062208 and R01AG055784, Johnson & Johnson Inc. for the STEM2DScholar's Award, the National Science Foundation under grant number ECCS-1943895 and ECCS-2246564.  \nContents  \nAcknowledgments ............................................................................................................. ii  \nContents ............................................................................................................................ iii  \nList of Figures................................................................................................................... iv  \nList of Tables ................................................................................................................... vii  \nList of Equations ............................................................................................................ viii  \nIntroduction....................................................................................................................... 1  \nRapid Biomarker Screening of Alzheimer’s Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy............................................... 6  \n2.1. Introduction .............................................................................................................. 7  \n2.2. Related Work............................................................................................................ 9  \n2.3. Our Method: Raman-machine Learning with Interpretability ................................. 9  \n2.4. Experiments and Results ......................................................","cbCaily2PZp2MvDR","https://ap.wps.com/l/cbCaily2PZp2MvDR","pdf",1872933,1,70,"English","en",105,"# Introduction\n## Rapid Biomarker Screening of Alzheimer’s Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy\n## Measuring Complex Refractive Index through Deep-learning-enabled Optical Reflectometry\n## Bibliography","[{\"question\":\"Why are high-dimensional Raman spectroscopy signals challenging to interpret in bio-samples?\",\"answer\":\"Bio-sample Raman signals are hard to interpret mainly due to their high dimensionality and the complexity of extracting meaningful molecular information from them.\"},{\"question\":\"How does the thesis enable rapid screening of Alzheimer’s disease biomarkers?\",\"answer\":\"It uses a platform that combines graphene-assisted Raman spectroscopy with machine-learning interpretation to perform rapid AD biomarker screening in animal brains.\"},{\"question\":\"What is ReflectoNet and what does it predict?\",\"answer\":\"ReflectoNet is a deep learning model for computational reflectometry that predicts complex refractive indices of thin films on complex substrates from reflectance spectra.\"}]","High-Dimensional Spectroscopy Analysis with Machine Learning Techniques - Thesis | PDF",1785722834,176,{"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},"high-dimensional-spectroscopy-analysis-with-machine-learning-techniques-thesis","",{"@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/high-dimensional-spectroscopy-analysis-with-machine-learning-techniques-thesis/119160/",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},"Why are high-dimensional Raman spectroscopy signals challenging to interpret in bio-samples?","Question",{"text":75,"@type":76},"Bio-sample Raman signals are hard to interpret mainly due to their high dimensionality and the complexity of extracting meaningful molecular information from them.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis enable rapid screening of Alzheimer’s disease biomarkers?",{"text":80,"@type":76},"It uses a platform that combines graphene-assisted Raman spectroscopy with machine-learning interpretation to perform rapid AD biomarker screening in animal brains.",{"name":82,"@type":73,"acceptedAnswer":83},"What is ReflectoNet and what does it predict?",{"text":84,"@type":76},"ReflectoNet is a deep learning model for computational reflectometry that predicts complex refractive indices of thin films on complex substrates from reflectance spectra.","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},[93,97,101,104,109,114,119,122,127,130,134],{"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":21,"slug":103},"Exam","exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]