{"title":"通过前馈神经网络模型预测CRISPR/Cas9基因组编辑的靶标和脱靶效应","authors":"Pavithra Nagendran, Gowtham Murugesan, Jeyakumar Natarajan","doi":"10.1002/med4.70016","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Clustered regularly interspaced short palindromic repeats —CRISPR-associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on- and off-target effects of CRISPR/Cas9, which is essential for ensuring the safety and efficiency of genetic modifications made using this technology.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>In this study, we used the SITE-Seq dataset, which comprises CRISPR targets, to classify sequences for both on- and off-target effects. To evaluate sequence pairs, we built a feedforward neural network (FNN) with 10 fully connected layers and compared its performance with that of other state-of-the-art models.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We showed that our FNN model attained an accuracy rate of 0.95, greatly improving prediction reliability for both on- and off-target effects compared with other methods.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This work contributes a valuable predictive modeling framework to the field of CRISPR research, addressing both on- and off-target effects in a unified manner, which is an essential requirement for the safe and effective application of genomic editing technologies.</p>\n </section>\n </div>","PeriodicalId":100913,"journal":{"name":"Medicine Advances","volume":"3 2","pages":"88-96"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/med4.70016","citationCount":"0","resultStr":"{\"title\":\"Anticipating On-Target and Off-Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model\",\"authors\":\"Pavithra Nagendran, Gowtham Murugesan, Jeyakumar Natarajan\",\"doi\":\"10.1002/med4.70016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Clustered regularly interspaced short palindromic repeats —CRISPR-associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on- and off-target effects of CRISPR/Cas9, which is essential for ensuring the safety and efficiency of genetic modifications made using this technology.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>In this study, we used the SITE-Seq dataset, which comprises CRISPR targets, to classify sequences for both on- and off-target effects. To evaluate sequence pairs, we built a feedforward neural network (FNN) with 10 fully connected layers and compared its performance with that of other state-of-the-art models.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We showed that our FNN model attained an accuracy rate of 0.95, greatly improving prediction reliability for both on- and off-target effects compared with other methods.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>This work contributes a valuable predictive modeling framework to the field of CRISPR research, addressing both on- and off-target effects in a unified manner, which is an essential requirement for the safe and effective application of genomic editing technologies.</p>\\n </section>\\n </div>\",\"PeriodicalId\":100913,\"journal\":{\"name\":\"Medicine Advances\",\"volume\":\"3 2\",\"pages\":\"88-96\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/med4.70016\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medicine Advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/med4.70016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicine Advances","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/med4.70016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
聚类规律间隔短回文重复序列-CRISPR-associated protein 9 （CRISPR/Cas9）是一种能够实现高精度基因组编辑的基因编辑技术。然而，很难预测CRISPR/Cas9的靶向和脱靶效应，这对于确保使用该技术进行基因修饰的安全性和有效性至关重要。在本研究中，我们使用了包含CRISPR靶点的SITE-Seq数据集，对靶向和脱靶效应的序列进行分类。为了评估序列对，我们建立了一个具有10个完全连接层的前馈神经网络（FNN），并将其性能与其他最先进的模型进行了比较。结果FNN模型的准确率达到了0.95，与其他方法相比，大大提高了对靶效应和脱靶效应的预测可靠性。结论本工作为CRISPR研究领域提供了一个有价值的预测建模框架，统一解决了靶向效应和脱靶效应，这是基因组编辑技术安全有效应用的基本要求。
Anticipating On-Target and Off-Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model
Background
Clustered regularly interspaced short palindromic repeats —CRISPR-associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on- and off-target effects of CRISPR/Cas9, which is essential for ensuring the safety and efficiency of genetic modifications made using this technology.
Methods
In this study, we used the SITE-Seq dataset, which comprises CRISPR targets, to classify sequences for both on- and off-target effects. To evaluate sequence pairs, we built a feedforward neural network (FNN) with 10 fully connected layers and compared its performance with that of other state-of-the-art models.
Results
We showed that our FNN model attained an accuracy rate of 0.95, greatly improving prediction reliability for both on- and off-target effects compared with other methods.
Conclusion
This work contributes a valuable predictive modeling framework to the field of CRISPR research, addressing both on- and off-target effects in a unified manner, which is an essential requirement for the safe and effective application of genomic editing technologies.
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