A systematic method for solving data imbalance in CRISPR off-target prediction tasks

被引:0
|
作者
Guan Z. [1 ]
Jiang Z. [1 ]
机构
[1] School of Computer Science and Technology, East China Normal University, Shanghai
关键词
CRISPR/Cas9; system; Data imbalance; Off-target prediction;
D O I
10.1016/j.compbiomed.2024.108781
中图分类号
学科分类号
摘要
Accurately identifying potential off-target sites in the CRISPR/Cas9 system is crucial for improving the efficiency and safety of editing. However, the imbalance of available off-target datasets has posed a major obstacle in enhancing prediction performance. Despite several prediction models have been developed to address this issue, there remains a lack of systematic research on handling data imbalance in off-target prediction. This article systematically investigates the data imbalance issue in off-target datasets and explores numerous methods to process data imbalance from a novel perspective. First, we highlight the impact of the imbalance problem on off-target prediction tasks by determining the imbalance ratios present in these datasets. Then, we provide a comprehensive review of various sampling techniques and cost-sensitive methods to mitigate class imbalance in off-target datasets. Finally, systematic experiments are conducted on several state-of-the-art prediction models to illustrate the impact of applying data imbalance solutions. The results show that class imbalance processing methods significantly improve the off-target prediction capabilities of the models across multiple testing datasets. The code and datasets used in this study are available at https://github.com/gzrgzx/CRISPR_Data_Imbalance. © 2024 Elsevier Ltd
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