Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.
Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.
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DOI：
10.3390/metabo12070605
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发表时间：
2022-06-29
期刊：
影响因子：
4.1
通讯作者：
中科院分区：
文献类型：
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代谢物注释一直是一个具有挑战性的问题，特别是在非靶向代谢组学研究中，通过液相色谱-质谱联用（LC-MS）。这在一定程度上是由于公共可用的光谱库的局限性，其中包括串联质谱（MS/MS）数据采集的一小部分已知的代谢物。机器学习提供了基于MS/MS数据预测分子指纹的机会。然后，预测的分子指纹可用于帮助对通过使用未知代谢物的前体质量或分子式获得的推定代谢物ID进行排序。该方法特别适用于注释其相应MS/MS谱缺失或无法与可访问谱库中的谱匹配的代谢物。我们研究了基于MS/MS获得的数据进行分子指纹预测的卷积神经网络（CNN）。我们使用了从莫纳存储库和NIST 20获得的超过680，000个MS/MS光谱，代表了约36，000种化合物，用于训练和测试我们的CNN模型。经过训练的CNN模型作为Python包MetFID实现。该软件包在GitHub上提供，用户可以输入他们的MS/MS谱和相应的推定代谢物ID，以获得代谢物的排名列表。与其他两种基于机器学习的工具（CSI：FingerID和ChemDistiller）相比，MetFID在使用CASMI 2016基准数据集对推定的代谢物ID进行排名方面实现了更好的性能。
Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated a convolutional neural network (CNN) for molecular fingerprint prediction based on data acquired by MS/MS. We used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20, representing about 36,000 compounds for training and testing our CNN model. The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller).
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影响因子：
9.5
作者：
Nguyen DH;Nguyen CH;Mamitsuka H
通讯作者：
Mamitsuka H
影响因子：
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作者：
O'Boyle NM;Morley C;Hutchison GR
通讯作者：
Hutchison GR
影响因子：
4.1
作者：
Fedorova, Elizaveta S.;Matyushin, Dmitriy D.;Buryak, Aleksey K.
通讯作者：
Buryak, Aleksey K.
影响因子：
6.2
作者：
Zhang, Xiaolei;Lin, Tao;Ying, Yibin
通讯作者：
Ying, Yibin
DOI：
10.1093/bioinformatics/bty080
发表时间：
2018-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
通讯作者：
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