[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124679-en":3,"doc-seo-124679-105":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},124679,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine learning methods for genomic high-content screen data analysis applied to deduce organization of endocytic network - Dissertation","The dissertation presents machine learning methodologies for analyzing genomic high-content screening data with the aim of inferring the organization of the endocytic network. It covers experimental foundations and an endocytic HCS dataset, then develops and evaluates multiple modeling approaches including feature selection, unsupervised clustering, latent-variable models, Bayesian networks, and neural networks. The results derive phenotypic components, predict pathway membership from parameter values, and reveal biologically meaningful relationships, such as dependencies between endocytosis and cell adhesion.","MAX-PLANCK-INSTITUT FÜR MOLEKULARE ZELLBIOLOGIE UND GENETIK FAKULTÄT INFORMATIK DER TECHNISCHEN UNIVERSITÄT DRESDEN  \nMachine learning methods for genomic high-content screen data analysis applied to deduce organization of endocytic network  \nDissertation  \nzur Erlangung des akademischen Grades  \nDoctor of Philosophy (Ph. D.)  \nvorgelegt von  \nKseniia Nikitina  \ngeboren am 13. Mai 1993 in Krasnojarsk, Russland  \nDresden, 2016-2020  \nContents  \nContents ...........................................................................................................3  \nObjectives ........................................................................................................7  \nChapter 1 Introduction ................................................................................... 10  \n1.1 High-content biological data ................................................................10  \n1.1.1 Different perturbation types for HCS.............................................10  \n1.1.2 Types of observations in HTS ........................................................11  \n1.1.3 Goals and outcomes of MP HTS....................................................11  \n1.1.4 An overview of the classical methods of analysis of biological HT  \nand HCS data ..................................................................................................... 13  \n1.2 Machine learning for systems biology .................................................16  \n1.2.1 Feature selection ............................................................................. 17  \n1.2.2 Unsupervised learning ....................................................................18  \n1.2.3 Supervised learning ........................................................................20  \n1.2.4 Artificial neural networks...............................................................22  \n1.3 Endocytosis as a system process ..........................................................24  \n1.3.1 Endocytic compartments and main players ...................................24  \n1.3.2 Relation to other cellular processes................................................26  \nChapter 2 Experimental and analytical techniques .......................................29  \n2.1 Experimental methods ..........................................................................29  \n2.1.1 RNA interference ...........................................................................29  \n2.1.2 Quantitative multiparametric image analysis.................................33  \n2.2 Detailed description of the endocytic HCS dataset ..............................35  \n2.2.1 Basic properties of the endocytic dataset .......................................38  \n2.2.2 Control subset of genes ..................................................................43  \n2.3 Machine learning methods ...................................................................45  \n2.3.1 Latent variables models..................................................................45  \n2.3.2 Clustering .......................................................................................51  \n2.3.3 Bayesian networks..........................................................................61  \n2.3.4 Neural networks .............................................................................69  \nChapter 3 Results ...........................................................................................74  \n3.1 Selection of labeled data for training and validation based on KEGG information about genes pathways .......................................................................74  \n3.2 Clustering of genes ...............................................................................76  \n3.2.1 Comparison of clustering techniques on control dataset ...............76  \n3.2.2 Clustering results ............................................................................79  \n3.3 Independent components as basic phenotypes ..................","cbCaifyYkjq0taMH","https://ap.wps.com/l/cbCaifyYkjq0taMH","pdf",16689389,1,220,"English","en",105,"# Objectives\n# Chapter 1 Introduction\n## High-content biological data\n## Machine learning for systems biology\n## Endocytosis as a system process\n# Chapter 2 Experimental and analytical techniques\n## Experimental methods\n## Detailed description of the endocytic HCS dataset\n## Machine learning methods\n# Chapter 3 Results\n## Selection of labeled data using KEGG pathway information\n## Clustering of genes\n## Independent components as basic phenotypes\n## Bayesian network on endocytic parameters\n## Neural networks\n## Biological results\n# Chapter 4 Discussion\n## Machine learning approaches for discovery of phenotypic patterns","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses how to analyze genomic high-content screening data to deduce how the endocytic network is organized.\"},{\"question\":\"Which major machine learning approaches are used?\",\"answer\":\"The work uses feature selection, clustering, latent-variable models, Bayesian networks, and neural networks including autoencoders and deep learning for motif discovery.\"},{\"question\":\"How are biological insights extracted from the models?\",\"answer\":\"The study derives phenotypic components, annotates genes based on revealed phenotypes, predicts pathways from endocytic parameter values, and identifies biologically relevant dependencies such as links between endocytosis and cell adhesion.\"}]","Machine learning methods for genomic high-content screen data analysis applied to deduce organization of endocytic network - Dissertation | 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