MSMCE: A Novel Representation Module for Classification of Raw Mass Spectrometry Data
Zhang, F.; Gao, B.; Wang, Y.; Guo, L.; Zhang, W.; Xiong, X.
Show abstract
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 the 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 introduce a "residual" connection 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 reduces computational resource consumption and enhances training efficiency, highlighting its effectiveness and generalizability in raw MS data classification.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images 95%
- Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics 95%
- Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics 95%
Similar papers in this journal
- Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data. 95%
- DeepNeuropePred: a robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model 94%
- Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach 93%
Similar papers in this journal
- mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography Mass Spectrometry Based Non-targeted Metabolomics Data Analysis 94%
- pDeep3: Towards More Accurate Spectrum Prediction with Fast Few-Shot Learning 94%
- spectrum_utils: A Python package for mass spectrometry data processing and visualization 93%
Similar papers in this journal
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics 94%
- Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline 93%
- A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.