优化者的困境:优化强烈影响在转录学预测模型的选择
Jake Crawford1, Maria Chikina2, Casey S Greene3,4
1Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
Bioinformatics advances
|January 29, 2024
概括
像坐标下降 (liblinear) 和随机梯度下降 (SGD) 这样的机器学习优化器在癌症基因预测中对LASSO逻辑回归具有可比性. 报告优化器的选择对于可重复性研究至关重要.
科学领域:
- 计算生物学是一种计算生物学.
- 机器学习 机器学习
- 基因组学就是基因组学.
背景情况:
- 机器学习模型在生物研究中被广泛使用,但优化算法的选择往往没有得到报道.
- 了解不同优化器对模型性能和可解释性的影响,对于可重现的科学发现至关重要.
研究的目的:
- 为了比较两个优化方法的性能和模型稀疏性,坐标下降 (liblinear) 和随机梯度下降 (SGD),用于LASSO逻辑回归.
- 从泛癌驱动基因的基因表达数据中预测突变状态和基因基本性.
主要方法:
- 应用LASSO逻辑回归使用Python的scikit-learn包与两个优化器:liblinear (坐标下降) 和SGD.
- 对每个优化器的各种规范化强度评估模型性能和稀疏性.
主要成果:
- 无论是liblinear还是SGD优化器都表现出了可比的性能.
- 无线模型需要更多的规范化调整,但在高模型稀疏度方面表现出色,而SGD模型需要学习速率调整,但在不同的稀疏度中表现出稳定性.
- 优化器的选择会影响模型调整和性能特征.
结论:
- 优化算法的选择显著影响LASSO逻辑回归模型在基因表达基因癌症驱动基因预测中的结果.
- 对于科学界来说,对优化器的明确报告至关重要,以确保结果的透明度和可重复性.
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