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负面的基准测试:负面数据生成对miRNA-mRNA相互作用的分类的影响
Efrat Cohen-Davidi1, Isana Veksler-Lublinsky1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
PLoS computational biology
|August 26, 2024
概括
产生微RNA-目标相互作用 (MTIs) 的负数据对于机器学习模型至关重要. 本研究分析了创建负数据的方法,揭示了它们对MTI预测准确性的影响,并突出了模型概括方面的挑战.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 微RNAs (miRNAs) 是小型非编码RNAs,通过与信使RNAs (mRNAs) 的基配对,在转录后调节基因表达.
- 对miRNA-目标相互作用 (MTIs) 的计算预测有助于识别实验验证的潜在目标.
- 用于MTI预测的机器学习 (ML) 方法需要积极和消极的训练示例,但目前无法生成高通量负数据.
研究的目的:
- 收集和研究各种方法来生成动物miRNA-目标相互作用的负数据.
- 使用机器学习模型评估不同负面数据生成策略对人类MTIs分类准确性的影响.
- 分析区分负数据集的特征,并评估一类分类模型对MTI预测的适用性.
主要方法:
- 收集了各种方法来生成动物miRNA-目标相互作用的负数据.
- 训练有素的ML模型使用固定的正数据集与不同的负数据集相结合.
- 通过数据集内部和数据集交叉评估,分析数据集特征并考虑一类分类方法来评估模型性能.
主要成果:
- 证明了负数据对MTI分类准确性的重要意义.
- 确定了区分负数据集和影响ML模型性能的特定方法特征.
- 从不同的数据生成方法获得的培训和测试集中概括ML模型的突出挑战.
结论:
- 选择负数据生成方法极大地影响了MTI预测性能.
- 需要对负数据生成进行标准化,以提高MTI预测研究的可靠性和可比性.
- 对强大的负数据生成和模型概括的进一步研究对于推进计算MTI预测至关重要.
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