GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data
Carbohydrate sequencing is a formidable task identified as a strategic goal in modern biochemistry. It relies on identifying a large number of isomers and their connectivity with high accuracy. Recently, gas phase vibrational laser spectroscopy combined with mass spectrometry tools have been propose...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Beilstein-Institut
2023-12-01
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| Series: | Beilstein Journal of Organic Chemistry |
| Subjects: | |
| Online Access: | https://doi.org/10.3762/bjoc.19.134 |
| _version_ | 1797323474745688064 |
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| author | Thomas Barillot Baptiste Schindler Baptiste Moge Elisa Fadda Franck Lépine Isabelle Compagnon |
| author_facet | Thomas Barillot Baptiste Schindler Baptiste Moge Elisa Fadda Franck Lépine Isabelle Compagnon |
| author_sort | Thomas Barillot |
| collection | DOAJ |
| description | Carbohydrate sequencing is a formidable task identified as a strategic goal in modern biochemistry. It relies on identifying a large number of isomers and their connectivity with high accuracy. Recently, gas phase vibrational laser spectroscopy combined with mass spectrometry tools have been proposed as a very promising sequencing approach. However, its use as a generic analytical tool relies on the development of recognition techniques that can analyse complex vibrational fingerprints for a large number of monomers. In this study, we used a Bayesian deep neural network model to automatically identify and classify vibrational fingerprints of several monosaccharides. We report high performances of the obtained trained algorithm (GlAIcomics), that can be used to discriminate contamination and identify a molecule with a high degree of confidence. It opens the possibility to use artificial intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications. |
| first_indexed | 2024-03-08T05:29:13Z |
| format | Article |
| id | doaj.art-ac9d215d9e7049ad979941334ae1f05d |
| institution | Directory Open Access Journal |
| issn | 1860-5397 |
| language | English |
| last_indexed | 2024-03-08T05:29:13Z |
| publishDate | 2023-12-01 |
| publisher | Beilstein-Institut |
| record_format | Article |
| series | Beilstein Journal of Organic Chemistry |
| spelling | doaj.art-ac9d215d9e7049ad979941334ae1f05d2024-02-06T09:08:29ZengBeilstein-InstitutBeilstein Journal of Organic Chemistry1860-53972023-12-011911825183110.3762/bjoc.19.1341860-5397-19-134GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans dataThomas Barillot0Baptiste Schindler1Baptiste Moge2Elisa Fadda3Franck Lépine4Isabelle Compagnon5Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth W23 F2H6, Ireland Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France Carbohydrate sequencing is a formidable task identified as a strategic goal in modern biochemistry. It relies on identifying a large number of isomers and their connectivity with high accuracy. Recently, gas phase vibrational laser spectroscopy combined with mass spectrometry tools have been proposed as a very promising sequencing approach. However, its use as a generic analytical tool relies on the development of recognition techniques that can analyse complex vibrational fingerprints for a large number of monomers. In this study, we used a Bayesian deep neural network model to automatically identify and classify vibrational fingerprints of several monosaccharides. We report high performances of the obtained trained algorithm (GlAIcomics), that can be used to discriminate contamination and identify a molecule with a high degree of confidence. It opens the possibility to use artificial intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications.https://doi.org/10.3762/bjoc.19.134bayesian neural networkdeep learningglycomicsirspectroscopy |
| spellingShingle | Thomas Barillot Baptiste Schindler Baptiste Moge Elisa Fadda Franck Lépine Isabelle Compagnon GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data Beilstein Journal of Organic Chemistry bayesian neural network deep learning glycomics ir spectroscopy |
| title | GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data |
| title_full | GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data |
| title_fullStr | GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data |
| title_full_unstemmed | GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data |
| title_short | GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data |
| title_sort | glaicomics a deep neural network classifier for spectroscopy augmented mass spectrometric glycans data |
| topic | bayesian neural network deep learning glycomics ir spectroscopy |
| url | https://doi.org/10.3762/bjoc.19.134 |
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