Predicting Lifespan-Extending Chemical Compounds with Machine Learning and Biologically Interpretable Features
Predicting Lifespan-Extending Chemical Compounds with Machine Learning and Biologically Interpretable Features
复制标题
DOI：
10.1101/2022.11.20.517230
复制
发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Caio Ribeiro;Christopher K. Farmer;J. de Magalhães;A. Freitas
中科院分区：
文献类型：
--
作者：
Caio Ribeiro;Christopher K. Farmer;J. de Magalhães;A. Freitas
最近，人们越来越有兴趣开发针对老龄化的药理学干预措施，以及利用机器学习来分析与老龄化有关的数据。在这项工作中，我们使用机器学习方法来分析来自DrugAge的数据，DrugAge是一个关于化合物(包括药物)调节模式生物寿命的数据库。为此，我们创建了四个数据集，用于预测化合物是否延长秀丽线虫(DrugAge中最常见的模式生物)的寿命，使用四种不同类型的预测生物学特征，基于化合物-蛋白质相互作用、化合物与衰老相关基因编码的蛋白质之间的相互作用，以及两种类型的针对化合物靶标蛋白质的标注术语，即来自WormBase的表型本体的基因本体(GO)术语和生理术语。为了分析这些数据集，我们在数据预处理阶段使用了特征选择方法的组合，并使用成熟的随机森林算法从选择的特征中学习预测模型。使用GO项和蛋白质相互作用因子作为特征学习两个最好的模型，预测准确率分别约为82%和80%。此外，我们还根据衰老的生物学解释了这两个最好的模型中最重要的特征，并从以前未标记的化合物列表中预测了最有希望延长寿命的新化合物。
Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as on the use of machine learning for analysing ageing-related data. In this work we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase’s Phenotype Ontology. To analyse these datasets we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. The two best models were learned using GO terms and protein interactors as features, with predictive accuracies of about 82% and 80%, respectively. In addition, we interpreted the most important features in those two best models in light of the biology of ageing, and we also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds.