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负采样如何影响转录因子结合站点预测模型的性能
Natan Tourne1, Gaetan De Waele1, Vanessa Vermeirssen2,3,4
1Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium.
选择正确的负采样方法对于准确的转录因子结合位点 (TFBS) 预测至关重要. 基于积极相似性的基因组采样表现最好,优于常见的二核酸混杂技术.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 转录因子 (TFs) 通过DNA结合来调节基因表达.
- 预测TF结合位点 (TFBS) 对于理解基因调节和发育至关重要.
- 对于TFBS预测的深度学习模型通常使用ChIP-seq数据,通常被视为积极样本.
研究的目的:
- 调查各种负采样技术对TFBS预测性能的影响.
- 评估常见的负采样策略,包括基因组,混合和邻里采样.
- 突出负数据选择在TFBS预测模型准确性中的关键作用.
主要方法:
- 使用ChIP-seq和ATAC-seq数据创建了高质量的测试数据集.
- 训练有素的预测模型使用基因组采样,混合,二核酸混合,邻居采样和细胞系特定采样.
- 模拟场景缺乏匹配的ATAC-seq数据来评估方法的稳定性.
主要成果:
- 训练数据集上的性能指标往往会夸大真正的模型性能.
- 基因组对阴性进行采样,基于与阳性的相似性,在测试的技术中产生了最好的结果.
- 代核酸混负面是一种常见的做法,导致模型性能差.
结论:
- 选择负采样技术显著影响TFBS预测模型的性能.
- 仔细考虑负面数据对于可靠的TFBS预测至关重要.
- 像二核酸混这样的标准实践对于TFBS预测可能不是最佳的.
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