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- W4385376564 abstract "Numerous models have been proposed to efficiently identify diverse unknown molecules generated in chemical experiments through mass spectrometry information. Nonetheless, molecular identification still encounters the bottleneck of low recognition rate. To address the issue, this study proposes a novel molecular fingerprint prediction method based on residual network (ResNet) with attention mechanism and a loss function to enhance the query performance of mass spectrometry databases. This method outperforms the current state-of-the-art techniques such as MetExpert and DeepEI in molecular identification tasks by achieving a higher recognition rate." @default.
- W4385376564 created "2023-07-30" @default.
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- W4385376564 date "2023-01-01" @default.
- W4385376564 modified "2023-09-23" @default.
- W4385376564 title "Molecular Identification Using Deep Learning Method" @default.
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- W4385376564 doi "https://doi.org/10.1007/978-981-99-4749-2_32" @default.
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