ArticleThe annals of applied statistics2022
Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.
Article in The annals of applied statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- Article
- Semi-supervised Bayesian integration of multiple spatial proteomics datasets.PLoS computational biology · 2025Article
- Inferring residue level hydrogen deuterium exchange with ReX.Communications chemistry · 2025Article
- Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.The annals of applied statistics · 2022Article
- Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.Nature communications · 2022Article
- A Bayesian semi-parametric model for thermal proteome profiling.Communications biology · 2021Article
- Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers.Nature communications · 2020Article
- A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using K-component mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. The availability of
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