[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120325-en":3,"doc-seo-120325-105":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":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},120325,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Predicting Alzheimer’s Disease from miRNA Sequence and Expression Data with Machine Learning - Thesis","This thesis develops machine learning strategies to predict Alzheimer’s Disease using miRNA sequence and expression information. It builds a k-mer bag-of-words representation from miRNA sequences, applies modeling in the Orange data mining platform, and evaluates multiple machine learning models across prepared datasets. The study compares performance across sequence types and k values, analyzes model performance using ROC-based metrics, and interprets learned k-mers to identify miRNA regions linked to disease involvement. Results include model selection guidance and biomarker-relevant insights for downstream research.","PREDICTING ALZHEIMER’S DISEASE FROM MIRNA SEQUENCE AND EXPRESSION DATA WITH MACHINE LEARNING  \nby  \nSydney Monserrate  \nA Thesis  \nSubmitted to the  \nGraduate Faculty  \nof  \nGeorge Mason University  \nin Partial Fulfillment of  \nThe Requirements for the Degree  \nof  \nMaster of Science  \nBiology  \nCommittee:  \n  Dr. Christopher Lockhart, Thesis Chair  \n  Dr. Ancha Baranova, Committee  \nMember  \nDr. Iosif Vaisman, Committee  \n__________________________________________  \nMember  \n__________________________________________  \nDr. Iosif Vaisman, Director,  \nSchool of Systems Biology  \n  Dr. Gerald L. R. Weatherspoon,  \nAssociate Dean for Undergraduate and Graduate Affairs, College of Science  \n  Dr. Fernando R. Miralles-Wilhelm,  \nDean, College of Science  \nDate:   Spring Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nPredicting Alzheimer’s Disease from miRNA Sequence and Expression Data with  \nMachine Learning  \nA Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at George Mason University  \nby  \nSydney Monserrate  \nBachelor of Science  \nVirginia Commonwealth University, 2021  \nDirector: Christopher Lockhart, Research Assistant Professor School of Systems Biology  \nSpring Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nCopyright 2024 Sydney Monserrate All Rights Reserved  \nii  \nDEDICATION  \nI dedicate this work to all the faculty, advisors, friends, family and peers who all supported me throughout the entire process.  \nACKNOWLEDGEMENTS  \nI wish to thank my professors and committee chair, Christopher Lockhart, for their dedication to my future.  \nTABLE OF CONTENTS  \nPage  \n[List of Tables ......................................................................................................................vi](List of Tables ......................................................................................................................vi)  \nList of Figures ................................................................................................................... vii  \nList of Abbreviations ....................................................................................................... viii  \nAbstract............................................................................................................................. iix  \nIntroduction ......................................................................................................................... 1  \nAlzheimer’s Disease ........................................................................................................ 1  \nExtracellular Vesicles ......................................................................................................2  \nPredicting AD with miRNA Data ....................................................................................4  \nMethods ...............................................................................................................................6  \nDatasets ............................................................................................................................6  \nk-Mer Bag of Words Model.............................................................................................7  \nOrange Data Mining Platform .........................................................................................8  \nMachine Learning Models ............................................................................................. 10  \nResults & Discussion ......................................................................................................... 11  \nPredicting Alzheimer’s Disease Involvement of miRNAs ............................................ 11  \nModel Preparation and Selection ............................................................................... 12  \nModel Performance .................................................................................................... 17  \nInterpretation of Model ","cbCaieWdRXOjH3vk","https://ap.wps.com/l/cbCaieWdRXOjH3vk","pdf",1939962,1,47,"English","en",105,"# Introduction\n## Alzheimer’s Disease\n## Extracellular Vesicles\n## Predicting AD with miRNA Data\n# Methods\n## Datasets\n## k-Mer Bag of Words Model\n## Orange Data Mining Platform\n## Machine Learning Models\n# Results & Discussion\n## Predicting Alzheimer’s Disease Involvement of miRNAs\n## Model Preparation and Selection\n## Model Performance\n## Interpretation of Model Results\n## Predicting Alzheimer’s Disease from miRNA Expression Data\n# Conclusions\n# References","[{\"question\":\"What type of data does the thesis use to predict Alzheimer’s Disease?\",\"answer\":\"The thesis uses miRNA sequence data represented through a k-mer bag-of-words approach, and it also evaluates miRNA expression data for prediction.\"},{\"question\":\"How are miRNA sequences converted for machine learning?\",\"answer\":\"miRNA sequences are transformed using a k-mer bag of words model, enabling machine learning algorithms to learn patterns from k-mer frequency information.\"},{\"question\":\"How is model performance assessed and interpreted?\",\"answer\":\"Model performance is evaluated using ROC-based metrics such as AUROC, and interpretability is performed by examining which k-mers contribute strongly to model importance.\"}]","Predicting Alzheimer’s Disease from miRNA Sequence and Expression Data with Machine Learning - 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