ReviewJournal of proteome research2015
Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.
Review in Journal of proteome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
159 citing papers in PubMed.
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- Genome-wide pQTL mapping in human skin identifies specific genetic regulators and mechanistic links to skin disorders.Nature communications · 2026Article
- SoftHybrid: A Hybrid Imputation Algorithm Optimized for Single-Cell Proteomics Data.Journal of proteome research · 2026Article
- Proteomic comparison of human neural cell-derived extracellular vesicles and parental cells from Alzheimer's disease and cognitively normal individuals.Science China. Life sciences · 2026Article
- Distinct Functional Signatures of Human Olfactory and Respiratory Mucus Revealed by Proteomics Combined With Machine Learning.Proteomics. Clinical applications · 2026Article
- Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell Nanodroplet Processing Proteomics Pipeline for Analysis of Human-Derived Microglia.Proteomics · 2026Article
- BioProEV: A Bioinformatics Pipeline for Biologically-Relevant Handling of Missing Values in the Analysis of Extracellular Vesicles by Mass Spectrometry.Journal of extracellular biology · 2026Article
- Molecular and physiological acclimation to low light and iron scarcity in a globally abundant oceanic pelagophyte.Nature communications · 2026Article
- Classification with Missing Data - AbioRxiv : the preprint server for biology · 2026Article
- Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration.Scientific reports · 2026Article
- Comprehensive evaluation of statistical approaches for differential metaproteomics.bioRxiv : the preprint server for biology · 2026Article
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data.Nature communications · 2026Article
- The role of histone demethylase PHF2 as a tumour suppressor in hepatocellular carcinoma by regulating SRXN1.Oncogenesis · 2026Article
- A postoperative recurrence prediction model for intrahepatic cholangiocarcinoma based on multi-omics analysis of adjacent-to-tumor tissues.Frontiers in oncology · 2026Article
- spammR: an R package designed for analysis and integration of spatial multi-omic measurements.Bioinformatics advances · 2026Article
- Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE).Proteomics. Clinical applications · 2026Article
- optTMT: optimizing any experimental design to minimize false positives caused by TMT reporter ion interference.Bioinformatics advances · 2026Article
- Proteomic Signatures of SARS-CoV-2 Susceptibility in Mexican Free-tailed Bats and Their Application to Viral Surveillance.Integrative and comparative biology · 2025Article
99 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.
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Registered trials
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.