Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features.
Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features.
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DOI：
10.18632/aging.204866
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发表时间：
2023-07-13
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影响因子：
--
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中科院分区：
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最近，人们对开发针对衰老的药物干预措施以及使用机器学习分析与衰老相关的数据越来越感兴趣。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是一个调节模型生物寿命的化合物（包括药物）数据库。为此，我们创建了四种类型的数据集，用于预测化合物是否延长C的寿命。elegans（DrugAge中最常见的模式生物），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，以及两种类型的术语注释化合物靶向的蛋白质，即基因本体论（GO）术语和WormBase表型本体论中的生理学术语。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，并使用了成熟的随机森林算法来从所选特征中学习预测模型。此外，我们根据衰老的生物学解释了两个最佳模型中最重要的特征。一个值得注意的特征是GO术语“谷氨酸代谢过程”，其在细胞氧化还原稳态和解毒中起重要作用。我们还预测了最有希望的新化合物，从以前未标记的化合物列表中延长寿命。这些药物包括硝普钠，它被用作抗高血压药物。总的来说，我们的工作为未来利用机器学习来预测新型延长寿命的化合物开辟了道路。
Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in 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 types of 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. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term “Glutathione metabolic process”, which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds.
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影响因子：
3.6
作者：
Goodman, Steven
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影响因子：
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通讯作者：
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