[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128457-en":3,"doc-seo-128457-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128457,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms for Predicting On-Target and Off-Target Effects of CRISPR Cas13d for gene editing - Findings and model comparison","CRISPR-Cas13 is an RNA editing system driven by single-stranded RNAs, where accurate prediction of on-target and off-target effects of CRISPR-Cas13d guides sgRNA design. This study compares the predictive performance of multiple machine learning algorithms using a reported dataset for Cas13d. Results indicate CatBoost as the most accurate model, showing high sensitivity across evaluation metrics. The work supports choosing modeling methods that efficiently capture RNA sequence features and can extend to other CRISPR systems for safer, more effective gene therapy development.","Comparative Analysis of Machine Learning Algorithms for Predicting On-Target and Off-Target Effects of CRISPR  \nCas13d for gene editing  \nJingze Liu1, Jiahao Ma2  \n1 Department of X, X University, City, State, Country  \n2 School of Marine science and technology, Harbin Institute of Technolgy, Weihai, Shandong,  \nChina  \nEmails: [sonnyliu98@outlook.com](sonnyliu98@outlook.com1)[1](sonnyliu98@outlook.com1); [2190790308@stu.hit.edu.cn](2190790308@stu.hit.edu.cn2)[2](2190790308@stu.hit.edu.cn2)  \nAbstract  \nCRISPR-Cas13 is a system that utilizes single stranded RNAs for RNA editing. Prediction of on-target and off-target effects for the CRISPR-Cas13d dependency enables us to design specific single guide RNAs (sgRNAs) that help locate the desired RNA target positions. In this study, we compared the performance of multiple machine learning algorithms in predicting these effects using a reported dataset. Our results show that Catboost is the most accurate model with high sensitivity. This finding represents a significant advancement in our understanding of how to chose modeling methods to deal with RNA sequence feaatures effictivelys. Furthermore , our approach can potentially be applied to other CRISPR systems and genetic engineering techniques. Overall, this work has important implications for developing safer and more effective gene therapies and biotechnological applications.  \nIntroduction  \nThe CRISPR-Cas13 system is a powerful tool for RNA editing that relies on the identification of efficient guide RNAs (gRNAs) to achieve specific gene modifications. However, the success of this technology depends heavily on the ability to accurately predict both on-target and off-target effects of gRNAs, which can affect the efficiency and safety of gene editing. Accurate predictions require the consideration of various factors, including the sequence features of the target RNA and the surrounding genomic context. Despite recent progress in the development of computational tools for gRNA design, there remains room for improvement in the accuracy and speed of current methods.  \nPrevious studies have demonstrated the importance of considering various parameters in gRNA design , including thermodynamic stability, secondary structure formation , and dinucleotide propensity. Computational methods for optimizing gRNA selection typically  \nrely on machine learning algorithms trained on large datasets of existing experimental data. Examples include Random Forest , Support Vector Machines, Artificial Neural Networks, Gradient Boosting Machines, and Extremely Randomized Trees. While some researchers have investigated the utility of deep learning techniques such as Convolutional Neural Networks (CNNs) , Recurrent Neural Networks ( RNNs) , and Rodom Forest ( RF) for gRNA optimization.  \nIn this study, we aim to evaluate the performance of several popular machine learning algorithms in predicting on-target and off-target effects of gRNAs using a publicly available dataset. We focus specifically on the Cas13d variant of the CRISPR-Cas13 system due to its promising properties, including higher cleavage activity and reduced immune cell activation compared to alternative variants. Understanding the effectiveness of different ML algorithms in predicting gRNA performance will improve our ability to optimize the delivery of therapeutics based on CRISPR-Cas13d, leading to better treatment outcomes and increased patient safety.  \nMethods  \nDataset and Preprocessing  \nThe dataset is [from public data source from bitbucket.org/weililab/deepcas13](from public data source from bitbucket.org/weililab/deepcas13) . Machine learning method  \nIn this study, we evaluate the performance of more than 20 popular machine learning algorithms - Logistic Regression ( LR) , Decision Tree ( DT) , Random Forest ( RF) , kNearest Neighbors ( k-NN) , Support Vector Machine (SVM) , and Catboost - for predicting on-target and off-target effects of sgRNAs designed for the CRISPR-Cas13d sy","cbCaidxsmHFL3SPw","https://ap.wps.com/l/cbCaidxsmHFL3SPw","pdf",290389,3,1,9,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Dataset and Preprocessing\n## Model Evaluation\n# Results and discussion","[{\"question\":\"What task does the study address for CRISPR-Cas13d?\",\"answer\":\"It predicts both on-target and off-target effects of sgRNAs for the CRISPR-Cas13d system to support RNA editing design.\"},{\"question\":\"Which machine learning algorithm performed best in the results?\",\"answer\":\"CatBoost (CatBoost Regressor) was selected as the best model after evaluating multiple metrics and weighting them using PyCaret.\"},{\"question\":\"Why are different machine learning algorithms evaluated in this work?\",\"answer\":\"The study compares algorithms with distinct modeling characteristics to identify strengths and weaknesses and to determine suitable methods for capturing RNA sequence-related features.\"}]","Comparative Analysis of Machine Learning Algorithms for Predicting On-Target and Off-Target Effects of CRISPR Cas13d for gene editing - 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