Abstract
The success of machine learning algorithms generally depends on effective features. Prediction of molecular substructure based on mass spectral data is try to extract more useful information or feature expression. In this paper, deep learning (DBN) was used to extract mass spectral features automatically. A large dataset consisting 11 molecular substructure is extracted from NIST mass spectral library. The experimental results show that deep learning (DBN) achieve best classification performance in 11 molecular substructure contrasting traditional classification methods.
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Acknowledgments
This work was supported by National Natural Science Foundation of China under grant nos. 61271098, 61472282, 61300058 and 61032007, and Provincial Natural Science Research Program of Higher Education Institutions of Anhui Province under grant no. KJ2012A005.
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Zhang, ZS., Cao, LL., Zhang, J., Chen, P., Zheng, Ch. (2015). Prediction of Molecular Substructure Using Mass Spectral Data Based on Deep Learning. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_52
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DOI: https://doi.org/10.1007/978-3-319-22186-1_52
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