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• W4313443340 endingPage "106489" @default.
• W4313443340 startingPage "106489" @default.
• W4313443340 abstract "sgRNAs on-target activity prediction is a critical step in the CRISPR-Cas9 system. Due to its importance to RNA function research and genome editing application, some computational methods were introduced, treating it as a binary classification task or a regression task. Among these methods, sgRNA-PSM is a state-of-the-art method. In this work, we improved this method by proposing a new feature extraction method called two-window-based PSM, which divides the DNA sequences into two non-overlapping segments so as to extract different patterns in the two different segments. The two-window-based PSM were fed into Support Vector Machines (SVMs), and a new method called sgRNA-2wPSM was proposed. Furthermore, a new oversampling method called SCORE-SVM-SMOTE was proposed to solve the imbalanced training set problem based on the SVM-SMOTE algorithm. Results on the benchmark datasets indicated that sgRNA-2wPSM is superior to other methods." @default.
• W4313443340 created "2023-01-06" @default.
• W4313443340 creator A5016991967 @default.
• W4313443340 creator A5018986249 @default.
• W4313443340 creator A5034219446 @default.
• W4313443340 date "2023-03-01" @default.
• W4313443340 modified "2023-09-24" @default.
• W4313443340 title "sgRNA-2wPSM: Identify sgRNAs on-target activity by combining two-window-based position specific mismatch and synthetic minority oversampling technique" @default.
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• W4313443340 doi "https://doi.org/10.1016/j.compbiomed.2022.106489" @default.
• W4313443340 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36841059" @default.
• W4313443340 hasPublicationYear "2023" @default.
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