COSIME:合作多视图集成和可扩展和可解释的模型解释器
bioRxiv : the preprint server for biology
|January 27, 2025
概括
科西姆集成了多学科数据,以获得全面的生物学见解. 这种新的方法增强了疾病预测,并解释了复杂的特征相互作用,为生物研究提供了可扩展和可解释的解决方案.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 单一omics研究提供有限的生物系统洞察力.
- 整合多种多学科数据带来了解释挑战.
研究的目的:
- 介绍COSIME (协作多视图集成和可扩展可解释模型解释器) 的多omics集成.
- 增强疾病表型预测和解释特征关系.
主要方法:
- 使用深度神经网络的可学习最佳运输 (LOT) 的反向传播.
- 采用沙普利值和沙普利-泰勒指数的蒙特卡洛抽样方法来评估特征的重要性和相互作用.
- 将COSIME应用于模拟和真实世界的数据集 (转录组学,表观组学,代谢组学).
主要成果:
- COSIME显著提高了跨多omics数据的预测性能.
- 提供复杂特征相互作用的增强解释性.
- 在阿尔茨海默病的空间位置中确定了协同作用的基因相互作用.
结论:
- COSIME提供了一个可扩展和可解释的解决方案,用于多omics集成.
- 通过特征相互作用分析,更深入地了解疾病机制.
- 开源COSIME工具可用于更广泛的研究应用.
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