生物预测-RPI:通过端到端机器学习实现非编码RNA和蛋白质之间的相互作用预测的民主化
Bruno Rafael Florentino1, Robson Parmezan Bonidia1,2, Natan Henrique Sanches1
1Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil.
Computational and structural biotechnology journal
|June 3, 2024
概括
生物预测-RPI是一个使用机器学习 (ML) 进行生物序列分析的新框架. 它简化了端到端的ML,可以准确地预测交互,而不需要专家知识.
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
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 算法对于在医疗保健和农业等领域从生物序列中提取知识至关重要.
- 生物序列往往是分类和非结构化的,需要为ML应用进行特征工程.
- 目前的端到端ML管道需要专门的用户专业知识,限制了更广泛的采用.
研究的目的:
- 引入BioPrediction-RPI,这是一个端到端的ML框架,用于识别生物序列之间的隐性相互作用.
- 为了实现准确的序列相互作用预测,而不需要专门的ML专业知识.
- 为用户提供可解释的报告,以了解预测见解.
主要方法:
- 生物预测-RPI使用特征工程来使用结构和拓特征来表示序列.
- 功能被组合在一起以训练部分模型,并将决策结合起来进行最终的预测.
- 该框架包括一个可解释性报告,以获得用户见解.
主要成果:
- 生物预测-RPI在12个数据集中展示了与专家创建的模型相比的竞争性性能.
- 该框架在40%至100%的实验案例中实现了同等或更高的性能.
- 它显示了模型与新数据微调的能力.
结论:
- 生物预测-RPI通过降低生物科学专业知识障碍,使端到端的ML民主化.
- 该框架的性能与ML专家相提并论,提高了可访问性和应用性.
- 它有助于识别复杂的序列相互作用,例如RNA-蛋白质对.
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