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Deep Metabolome: Applications of deep learning in metabolomics
Computational and Structural Biotechnology Journal ( IF 6 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.csbj.2020.09.033
Yotsawat Pomyen , Kwanjeera Wanichthanarak , Patcha Poungsombat , Johannes Fahrmann , Dmitry Grapov , Sakda Khoomrung

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.



中文翻译:

深度代谢组学:深度学习在代谢组学中的应用

在过去的几年中,深度学习已成功应用于各种组学数据。但是,与其他组学相比,深度学习在代谢组学中的应用仍然相对较低。当前,使用卷积神经网络架构的数据预处理似乎从深度学习中受益最大。使用人工神经网络/深度学习进行化合物/结构识别和定量分析的效果要优于传统的机器学习技术,而在生物学解释中只能观察到略微更好的结果。在将深度学习有效地应用于代谢组学之前,应解决一些挑战,包括特定于代谢组学的深度学习架构,维度问题和模型评估机制。

更新日期:2020-10-02
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