MSMCE: A novel representation module for classification of raw mass spectrometry data
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| Publicado en: | PLoS One vol. 20, no. 8 (Aug 2025), p. e0321239 |
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| Autor principal: | |
| Otros Autores: | , , , , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0321239 |2 doi | |
| 035 | |a 3237430616 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Zhang, Fengyi | |
| 245 | 1 | |a MSMCE: A novel representation module for classification of raw mass spectrometry data | |
| 260 | |b Public Library of Science |c Aug 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Mass spectrometry (MS) analysis plays a crucial role in the biomedical field; however, the high dimensionality and complexity of MS data pose significant challenges for feature extraction and classification. Deep learning has become a dominant approach in data analysis, and while some deep learning methods have achieved progress in MS classification, their feature representation capabilities remain limited. Most existing methods rely on single-channel representations, which struggle to effectively capture structural information within MS data. To address these limitations, we propose a Multi-Channel Embedding Representation Module (MSMCE), which focuses on modeling inter-channel dependencies to generate multi-channel representations of raw MS data. Additionally, we implement a feature fusion mechanism by concatenating the initial encoded representation with the multi-channel embeddings along the channel dimension, significantly enhancing the classification performance of subsequent models. Experimental results on four public datasets demonstrate that the proposed MSMCE module not only achieves substantial improvements in classification performance but also enhances computational efficiency and training stability, highlighting its effectiveness in raw MS data classification and its potential for robust application across diverse datasets. | |
| 653 | |a Mass spectrometry | ||
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Data analysis | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Scientific imaging | ||
| 653 | |a Modules | ||
| 653 | |a Machine learning | ||
| 653 | |a Representations | ||
| 653 | |a Efficiency | ||
| 653 | |a Datasets | ||
| 653 | |a Neural networks | ||
| 653 | |a Design | ||
| 653 | |a Mass spectroscopy | ||
| 653 | |a Algorithms | ||
| 653 | |a Embedding | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Gao, Boyong | |
| 700 | 1 | |a Wang, Yinchu | |
| 700 | 1 | |a Guo, Lin | |
| 700 | 1 | |a Zhang, Wei | |
| 700 | 1 | |a Xiong, Xingchuang | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 8 (Aug 2025), p. e0321239 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3237430616/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3237430616/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3237430616/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |