ArticleNature communications2020
Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning.Nature communications · 2026Article
- Negative-Ion Mode MALDI-TOF MS Combined with Machine Learning for the Rapid Identification of Colistin-ResistantACS omega · 2026Article
- Comparative analysis of continuous similarity measures for compound identification in mass spectrometry-based metabolomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2025Article
- A soft magnetoelastic sensor to decode levels of fatigue.Nature electronics · 2025Article
- MSMCE: A novel representation module for classification of raw mass spectrometry data.PloS one · 2025Article
- Article
- Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review.Mass spectrometry (Tokyo, Japan) · 2025Review
- MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.Analytical chemistry · 2024Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management.Cell reports. Medicine · 2024Article
- A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes.International journal of molecular sciences · 2024Review
- Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification.Analytical chemistry · 2023Article
- An end-to-end deep learning framework for translating mass spectra to de-novo molecules.Communications chemistry · 2023Article
- Article
- Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat.Foods (Basel, Switzerland) · 2022Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Transfer learning for non-image data in clinical research: A scoping review.PLOS digital health · 2022Article
- Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns.Metabolites · 2021Review
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available.
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