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CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction

As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this pr...

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Detalles Bibliográficos
Autores principales: Li, Bohao, Ai, Dongmei, Liu, Xiuqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945678/
https://www.ncbi.nlm.nih.gov/pubmed/35327601
http://dx.doi.org/10.3390/biom12030409
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author Li, Bohao
Ai, Dongmei
Liu, Xiuqin
author_facet Li, Bohao
Ai, Dongmei
Liu, Xiuqin
author_sort Li, Bohao
collection PubMed
description As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this process more accurate and efficient. Although there are already many sophisticated machine learning or deep learning models to predict the on-target cleavage efficiency of sgRNA, prediction accuracy remains to be improved. XGBoost is good at classification as the ensemble model could overcome the deficiency of a single classifier to classify, and we would like to improve the prediction efficiency for sgRNA on-target activity by introducing XGBoost into the model. We present a novel machine learning framework which combines a convolutional neural network (CNN) and XGBoost to predict sgRNA on-target knockout efficacy. Our framework, called CNN-XG, is mainly composed of two parts: a feature extractor CNN is used to automatically extract features from sequences and predictor XGBoost is applied to predict features extracted after convolution. Experiments on commonly used datasets show that CNN-XG performed significantly better than other existing frameworks in the predicted classification mode.
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spelling pubmed-89456782022-03-25 CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction Li, Bohao Ai, Dongmei Liu, Xiuqin Biomolecules Article As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this process more accurate and efficient. Although there are already many sophisticated machine learning or deep learning models to predict the on-target cleavage efficiency of sgRNA, prediction accuracy remains to be improved. XGBoost is good at classification as the ensemble model could overcome the deficiency of a single classifier to classify, and we would like to improve the prediction efficiency for sgRNA on-target activity by introducing XGBoost into the model. We present a novel machine learning framework which combines a convolutional neural network (CNN) and XGBoost to predict sgRNA on-target knockout efficacy. Our framework, called CNN-XG, is mainly composed of two parts: a feature extractor CNN is used to automatically extract features from sequences and predictor XGBoost is applied to predict features extracted after convolution. Experiments on commonly used datasets show that CNN-XG performed significantly better than other existing frameworks in the predicted classification mode. MDPI 2022-03-07 /pmc/articles/PMC8945678/ /pubmed/35327601 http://dx.doi.org/10.3390/biom12030409 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Bohao
Ai, Dongmei
Liu, Xiuqin
CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
title CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
title_full CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
title_fullStr CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
title_full_unstemmed CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
title_short CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
title_sort cnn-xg: a hybrid framework for sgrna on-target prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945678/
https://www.ncbi.nlm.nih.gov/pubmed/35327601
http://dx.doi.org/10.3390/biom12030409
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