EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution
| Autor: | Xiaowen Liu, Abdul Rehman Basharat, Xia Ning |
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| Rok vydání: | 2020 |
| Předmět: |
010402 general chemistry
Mass spectrometry 01 natural sciences Convolutional neural network Article Mass Spectrometry Analytical Chemistry Machine Learning Animals Humans Databases Protein Spectral data Zebrafish Ovarian Neoplasms Chemistry business.industry Extramural 010401 analytical chemistry Brain Pattern recognition Function (mathematics) 0104 chemical sciences Mass spectrum Female Neural Networks Computer Artificial intelligence Deconvolution Monoisotopic mass business |
| Zdroj: | Anal Chem |
| ISSN: | 1520-6882 0003-2700 |
| Popis: | Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation. |
| Databáze: | OpenAIRE |
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