[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125833-en":3,"doc-seo-125833-105":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},125833,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Formal Concept Analysis for Image Classification and Machine Learning Models for Anti-CRISPR Protein Discovery in Bioinformatics","This dissertation advances two bioinformatics needs: improving transparency in medical image analysis and accelerating discovery of Anti-CRISPR (Acr) proteins for more precise CRISPR-Cas gene editing. It addresses limitations of CNN interpretability methods by using Formal Concept Analysis (FCA) to model the relationship between abstract features and class labels across datasets. FCA-based performance is validated on MNIST and histopathological datasets. The study also leverages genomic context to mitigate Acr data scarcity and build machine learning models for new Anti-CRISPR discovery, while 3D structure analysis supports protein classification by Acr type and CRISPR-Cas system.","University of Nebraska-Lincoln  \nDigitalCommons@University of Nebraska-Lincoln  \n\n| Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2024– | Graduate Studies |\n| --- | --- |\n| 12-5-2023\u003Cbr>Formal Concept Analysis for Image Classification and Machine Learning Models for Anti-CRISPR Protein Discovery in Bioinformatics\u003Cbr>Minal Khatri\u003Cbr>University of Nebraska-Lincoln, [khatri.cs16@gmail.com](khatri.cs16@gmail.com)\u003Cbr>Follow this and additional works at: [https://digitalcommons.unl.edu/dissunl](https://digitalcommons.unl.edu/dissunl)\u003Cbr> Part of the Computer Sciences Commons |  |\n\nRecommended Citation  \nKhatri, Minal, \"Formal Concept Analysis for Image Classification and Machine Learning Models for AntiCRISPR Protein Discovery in Bioinformatics\" (2023) . Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2024–. 47.  \n[https://digitalcommons.unl.edu/dissunl/47](https://digitalcommons.unl.edu/dissunl/47)  \nThis Dissertation is brought to you for free and open access by the Graduate Studies at  \nDigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2024– by an authorized administrator of  \nDigitalCommons@University of Nebraska-Lincoln.  \nFORMAL CONCEPT ANALYSIS FOR IMAGE CLASSIFICATION AND MACHINE LEARNING MODELS FOR ANTI-CRISPR PROTEIN DISCOVERY IN BIOINFORMATICS  \nby  \nMinal Khatri  \nA DISSERTATION  \nPresented to the Faculty of  \nThe Graduate College at the University of Nebraska In Partial Fulfillment of Requirements  \nFor the Degree of Doctor of Philosophy  \nMajor: Computer Science  \nUnder the Supervision of Professors Jitender S. Deogun & Yanbin Yin  \nLincoln, Nebraska  \nDecember, 2023  \nFORMAL CONCEPT ANALYSIS FOR IMAGE CLASSIFICATION AND MACHINE LEARNING MODELS FOR ANTI-CRISPR PROTEIN DISCOVERY IN BIOINFORMATICS  \nMinal Khatri, Ph.D.  \nUniversity of Nebraska, 2023  \nAdviser: Professors Jitender S. Deogun & Yanbin Yin  \nThis study investigates two critical areas in bioinformatics: enhancing transparency in medical image analysis and advancing the discovery of Anti-CRISPR (Acr) proteins, which have potential in developing more precise and controlled CRISPR-Cas gene editing tools. While CNN’s are increasingly applied in critical fields like medical diagnosis, understanding their decision-making process remains a challenge. Although visualization techniques like Saliency maps offer insights into CNN’s decision-making for individual images, they do not explicitly establish a relationship between the high-level features learned by CNN’sand the class labels across dataset. To bridge this gap, Formal Concept Analysis (FCA) framework is leveraged as a image classification model, establishing a novel method for understanding the relationship between abstract features and class labels in medical imaging. The model’s performance is validated across a range from the simpler MNIST dataset to more complex histopathological image datasets like Warwick-QUand BreakHIS. Simultaneously, the study explores the genomic context of Acr genes and the 3D structure of Acr proteins for Acr discovery, which are not extensively explored in current bioinformatics tools. By leveraging genomic context, we overcome data scarcity and develop a machine learning model capable of discovering new Anti-CRISPRs. Additionally, the 3D structure analysis aids in developing machine learning classifiersto classify proteins by Acr type and CRISPR-Cas systems. Overall, this research makes significant contributions to the field of bioinformatics, by developing robust methodologies, enhancing our understanding of medical image analysis and advances Acr protein discovery.  \niii  \nACKNOWLEDGMENTS  \nI extend my deepest gratitude to my advisors, Dr. Jitender Deogun and Dr. Yanbin Yin. Their exceptional expertise, understanding, and patience have profoundly enriched my graduate experience. I am immensely thankful for their genero","cbCaio1DW9OjczjC","https://ap.wps.com/l/cbCaio1DW9OjczjC","pdf",7111506,1,59,"English","en",105,"# Contents\n## Introduction\n## Image Analysis\n## Discovery of Anti-CRISPR proteins\n## Literature Review\n## Image Classification Models\n## Formal Concept Analysis (FCA)\n## Bioinformatics Tools for Discovery of Anti-CRISPR Proteins\n## Formal Concept Analysis (FCA) Classifier\n## Application of FCA classifier on MNIST Dataset","[{\"question\":\"How does the dissertation improve transparency in medical image classification beyond saliency maps?\",\"answer\":\"It uses a Formal Concept Analysis (FCA) framework to explicitly connect abstract features learned from images to class labels across datasets, rather than focusing on per-image visual explanations.\"},{\"question\":\"Which datasets are used to validate the FCA image classification approach?\",\"answer\":\"The performance is validated on MNIST and on more complex histopathological image datasets such as Warwick-QUand BreakHIS.\"},{\"question\":\"How does the research address data scarcity in Anti-CRISPR (Acr) protein discovery?\",\"answer\":\"It leverages the genomic context of Acr genes to enable machine learning models that can discover new Anti-CRISPR proteins despite limited labeled data.\"}]","Formal Concept Analysis for Image Classification and Machine Learning Models for Anti-CRISPR Protein Discovery in Bioinformatics | 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