模拟和经验评估生物信息神经网络性能
Gwen A Miller1,2,3, Ahmed Roman1,2,3, Marc Glettig1,2,4
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
生物信息神经网络 (BiNN) 显示出对生物数据分析的前景. 数据集的特征,如样本大小和特征稀疏性,显著影响了BiNN的性能,特别是在信号强度有限的情况下.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 生物信息神经网络 (BiNN) 为生物数据提供可解释的深度学习.
- 了解数据集要求对于最佳BiNN性能至关重要.
- 像P-NET这样的先前模型使用体质数据预测前列腺癌转移.
研究的目的:
- 开发模拟框架,用于评估影响BiNN性能的因素.
- 评估结合生殖系和体质数据对预测前列腺癌转移状态的影响.
- 提供一个基于原则的方法,用于对比BiNN和理解它们的数据依赖性.
主要方法:
- 开发了两个模拟框架,以在不同条件下测试BiNN性能 (信号类型,强度,稀疏性,样本大小).
- 在P-NET模型中经验地整合了生殖系和体质数据.
- 评估模型预测准确性,基因优先级和可解释性.
主要成果:
- BiNN的性能受到小样本大小,弱信号强度和高特征稀疏度的限制.
- 双NN优先使用线性而不是非线性信号.
- 在稀疏的生殖基因数据上,P-NET表现不佳;整合生殖基因数据并没有改善预测,但增强了解释.
结论:
- 模拟框架可以系统地评估影响BiNN的数据集特征.
- 数据集属性显著影响生物信息神经网络的成功.
- 需要进一步的研究来优化BiNNs复杂的生物数据,特别是稀疏的基因组信息.
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