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负采样策略影响了与机器学习无尺度生物分子网络相互作用的预测

Pengpai Li¹, Bowen Shao¹, Guoqing Zhao¹

¹Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China.

|May 9, 2025

概括

蛋白质分子相互作用的机器学习 (ML) 模型被生物网络属性所倾向. 一个新的平衡度分布 (DDB) 采样策略提供了对生物信息学ML模型性能的更公平的评估.

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:

蛋白质分子相互作用对于生物过程至关重要.
机器学习 (ML) 被广泛用于预测这些相互作用.
现有的ML模型通常由于未解决的网络偏差而表现过于乐观.

研究的目的:

调查蛋白质分子相互作用的ML模型中的偏差.
了解生物网络属性的影响,比如无尺度拓,对ML模型性能.
开发一种方法,以更准确和公平地评估 ML 模型在这个领域.

主要方法:

在各种任务,数据集和方法中分析ML模型性能.
检查来自无尺度网络属性的偏差.
开发和应用平衡度分布 (DDB) 抽样策略.

主要成果:

生物网络拓学显著影响ML模型培训和评估.
无尺度性质在预测蛋白质分子相互作用时引入了固有的偏差.
该DDB采样策略有效地减轻了与网络属性相关的偏见.

结论:

DDB采样方法提供了对ML模型能力的更公平,更精确的评估.
解决网络偏差对于生物信息学中可靠的ML模型性能至关重要.
这项工作促进了对蛋白质分子相互作用预测的更严格的评估框架.

关键词:

生物网络是生物网络.机器学习是机器学习.负采样采集负采样采集蛋白质分子相互作用

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