学习生物可解释的基因表达数据的潜伏表示:路径活动得分学习算法
Ioulia Karagiannaki1, Krystallia Gourlia2, Vincenzo Lagani3,4
1Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (IESL-FORTH), Heraklion, Greece.
概括
我们开发了Pathway Activity Score Learning (PASL),一种用于减少基因表达数据维度的新方法. PASL创建可解释的路径活动得分,在预测准确度方面超过现有方法.
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
- 系统生物学 系统生物学
背景情况:
- 高维分子基因表达数据集对分析提出了挑战.
- 现有的缩小维度的方法往往缺乏直接的生物学解释性.
- 在复杂的数据集中识别有意义的生物模式至关重要.
研究的目的:
- 引入路径活动得分学习 (PASL),一种新的维度减小算法.
- 开发一种产生可解释的特征,代表路径活动的方法.
- 为了提高基因表达数据的预测性能和生物洞察力.
主要方法:
- 开发了路径活动分数学习 (PASL) 算法来减少维度.
- 构建的特征可以直接解释为路径活动得分.
- 在一个大群体 (5万个样本) 上训练PASL,以创建一个通用的基因表达特征字典.
- 在35,643个持有样本上验证了字典,并将其应用于165个不同的疾病数据集.
- 使用AutoML工具JADBio来评估信息保留后的转换.
主要成果:
- 与最先进的方法 (PLIER) 相比,PASL在乳腺癌和白血病数据集上的预测性能优越.
- 通过PASL构建的通用功能词典被验证为重建准确性.
- 预测信息在各种疾病中保留在PASL转换的特征空间中.
- 帕斯尔的潜伏空间提供了直接的生物学解释,与PCA等传统方法不同.
结论:
- PASL提供了一种有效和可解释的方法来减少基因表达数据的维度.
- 该算法增强了复杂分子数据的预测建模和生物理解.
- 通过PASL,可以轻松创建适用于各种生物环境的通用特征表示.
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