[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121886-en":3,"doc-seo-121886-105":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},121886,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning Methods for MicroRNA Target Prediction - PhD Thesis","MicroRNAs are small non-coding RNAs that regulate gene expression after transcription by repressing translation and accelerating messenger RNA degradation. Animal miRNA:mRNA binding is highly context-dependent, creating complex determinants of interaction specificity and effectiveness. This thesis presents miRsight, a machine-learning target prediction tool trained on 44 target recognition features from microRNA-transfected RNA-seq datasets. A hosted database supports search, filtering, and export, and results validate improved ranked target identification over TargetScan, MirTarget, and DIANA-microT.","Machine Learning Methods for MicroRNA Target Prediction  \nRyan Phelan  \nSchool of Biological Sciences  \nUniversity of East Anglia  \nA thesis submitted for the degree of Doctor of Philosophy  \nSeptember 2023  \nThis copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that use of any information derived therefrom must be in accordance with current UK Copyright Law. In addition, any quotation or extract must include full attribution.  \nAcknowledgements  \nI would like to thank my supervisor Simon Moxon for providing me with this opportunity and guiding me throughout my MSc and PhD. I would also like to thank my family and friends for years of encouragement and support, in particular my parents, Jamie and Leigh, and my brother, Josh.  \nFunding  \nThis work was supported by the UKRI Biotechnology and Biological Sciences Research Council Norwich Research Park Biosciences Doctoral Training Partnership [Grant number BB/M011216/1] .  \nThe research presented in this paper was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.  \nAbstract  \nMicroRNAs are small non-coding RNA molecules that form a post-transcriptional layer of gene regulation. microRNA binds with messenger RNA in order to repress translation and accelerate its degradation, ultimately downregulating the expression of genes. The mechanics of these bindings in animals are complex and entrenched in a myriad of contextual factors which influence the specificity and efficacy of potential interactions.  \nThis thesis describes the development of miRsight, a novel target prediction tool utilising advanced machine learning techniques. miRsight is trained using 44 target recognition features compiled through testing on published microRNA-transfected RNA sequencing data, an experimental procedure in which microRNA molecules are introduced into a sample to quantify their impact on gene expression. In addition to the tool itself, a database of pre-computed predictions is hosted at [https://mirsight. info](https://mirsight. info), which also provides search, filter, and export functionality for user convenience.  \nThe results of this study indicate that miRsight is able to more effectively predict and rank microRNA targets compared to popular target prediction tools. This is validated by examining the downregulation of gene expression from predicted targets using microRNA transfection. In the 12 samples reserved for testing, miRsight is shown to more consistently identify true targets in the top 100, 300 and 500 of predictions by rank compared to TargetScan, MirTarget and DIANA-microT. Additionally, miRsight is capable of producing several thousand total predictions for each microRNA while maintaining this high rate of prediction accuracy.  \nAccess Condition and Agreement  \nEach deposit in UEA Digital Repository is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the Data Collections is not permitted, except that material may be duplicated by you for your research use or for educational purposes in electronic or print form. You must obtain permission from the copyright holder, usually the author, for any other use. Exceptions only apply where a deposit may be explicitly provided under a stated licence, such as a Creative Commons licence or Open Government licence.  \nElectronic or print copies may not be offered, whether for sale or otherwise to anyone, unless explicitly stated under a Creative Commons or Open Government license. Unauthorised reproduction, editing or reformatting for resale purposes is explicitly prohibited (except where approved by the copyright holder themselves) and UEA reserves the right to take immediate ‘take down’ action on behalf of the copyright and/or rights holder if this Access condition of the UEA Digi","cbCainz1lo00y9AW","https://ap.wps.com/l/cbCainz1lo00y9AW","pdf",8499765,1,234,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Background\n## 2.1 Nucleic Acids\n## 2.2 Genetic Regulatory Network\n## 2.3 Genetic Sequencing\n## 2.4 Mechanics of miRNA:mRNA Binding\n## 2.5 Computational Prediction of miRNA Targets","[{\"question\":\"What problem does the thesis address in microRNA research?\",\"answer\":\"It addresses how to predict and rank microRNA targets despite complex, context-dependent miRNA:mRNA binding mechanics.\"},{\"question\":\"How is miRsight trained and what data are used?\",\"answer\":\"miRsight is trained using 44 target recognition features derived from tests on published microRNA-transfected RNA sequencing datasets.\"},{\"question\":\"How does miRsight perform compared with existing target prediction tools?\",\"answer\":\"In the held-out testing samples, it more consistently identifies true targets within the top ranks (100, 300, and 500) compared with TargetScan, MirTarget, and DIANA-microT.\"}]","Machine Learning Methods for MicroRNA Target Prediction - 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