ArticleNature communications2024
Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- SoftHybrid: A Hybrid Imputation Algorithm Optimized for Single-Cell Proteomics Data.Journal of proteome research · 2026Article
- Label-Free Targeted Proteomics Data Analysis Workflow Selection - Benchmarking AI-based and Data-Driven Approaches.Molecular & cellular proteomics : MCP · 2026Article
- msBayesImpute as a versatile framework for addressing missing values in biomedical mass spectrometry proteomics data.Communications chemistry · 2026Article
- omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.Nature communications · 2026Article
- Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.Proteomics · 2026Article
- Harnessing Proteogenomics to Advance Precision Oncology: From Melanoma and Hepatocellular Carcinoma Perspective.Current oncology reports · 2026Review
- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Spatial protein expression patterns across pathologically-associated fibers revealed molecular specialization in inclusion body myositis.Cell communication and signaling : CCS · 2026Article
- Serum Protein Signatures for Breast Cancer Detection in Treatment-Naïve African American Women Using Integrated Proteomics and Pattern Analysis.Sensors (Basel, Switzerland) · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- spammR: an R package designed for analysis and integration of spatial multi-omic measurements.Bioinformatics advances · 2026Article
- Proteomics Data Imputation With a Deep Model That Learns From Many Datasets.Molecular & cellular proteomics : MCP · 2025Article
- cpiVAE: Robust and Interpretable Cross-Platform Proteomics Imputation.bioRxiv : the preprint server for biology · 2025Article
- Data Processing and Analysis in Positional Proteomics.Proteomics · 2025Review
- Accurate diagnosis of hemoglobinopathies with machine learning based on high-throughput proteomics.HemaSphere · 2025Article
- The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis.Nature communications · 2025Article
- Salivary small extracellular vesicles reveal protein signatures in young patients with coronary artery disease.Clinical proteomics · 2025Article
- Statistical Methods for Multi-Omics Analysis in Neurodevelopmental Disorders: From High Dimensionality to Mechanistic Insight.Biomolecules · 2025Review
- Single-cell proteomics using mass spectrometry.Cell genomics · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.