对预测 lncRNA 亚细胞局部化的机器学习模型的评估
Jason R Miller1,2, Weijun Yi2, Donald A Adjeroh2
1Department of Computer Science and Information Technology; Hood College, Frederick, MD 21701, USA.
NAR genomics and bioinformatics
|September 19, 2024
概括
以前的研究过度估计了长非编码RNA (lncRNA) 细胞下局部化预测的准确性. 我们的研究结果显示,当前的模型在未过的数据上表现不佳,这表明任务比以前认为的要复杂得多.
科学领域:
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 长非编码RNAs (lncRNAs) 在细胞过程中起着至关重要的作用.
- 准确预测 lncRNA 亚细胞定位对于理解它们的功能至关重要.
- 现有的机器学习模型在lncRNA本地化预测方面表现中等.
研究的目的:
- 重新评估机器学习模型对于 lncRNA 亚细胞定位的性能.
- 调查数据过方法对模型性能的影响.
- 为未来的 lncRNA 局部化预测研究建立一个基准.
主要方法:
- 利用了IncATLAS数据库,其中包含来自15个人类细胞系的IncRNA丰度数据.
- 应用各种机器学习模型来预测 lncRNA 亚细胞定位.
- 在过 ("中间排除") 与未过的数据集上比较模型性能.
主要成果:
- "中间排除"数据过方法膨胀了性能指标,而没有改善真正的模型性能.
- 在未过的lncRNA数据上评估的模型只能达到60%左右的准确性.
- 由于数据过偏差,以前的报告可能高估了模型性能.
结论:
- 从核酸序列预测lncRNA亚细胞定位比以前认为的更具挑战性.
- "中间排斥"协议是误导性的,在未来的评估中应该避免使用它.
- 为了在这个领域取得可靠的进展,需要一个标准化的基准模型和评估程序.
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