ArticlePLoS computational biology2016
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessing sub-cellular resolution in spatial proteomics experiments.Current opinion in chemical biology · 2019Pooled it
- Subcellular proteomics of the protist Paradiplonema papillatum reveals the digestive capacity of the cell membrane and the plasticity of peroxisomes across euglenozoans.PLoS biology · 2025Article
- Semi-supervised Bayesian integration of multiple spatial proteomics datasets.PLoS computational biology · 2025Article
- Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing.PLoS computational biology · 2024Article
- SOS1 tonoplast neo-localization and the RGG protein SALTY are important in the extreme salinity tolerance of Salicornia bigelovii.Nature communications · 2024Article
- TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics.Briefings in bioinformatics · 2024Article
- 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
- The heterogeneous pharmacological medical biochemical network PharMeBINet.Scientific data · 2022Article
- Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line.Nature communications · 2021Article
- A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation.Frontiers in molecular neuroscience · 2021Article
- Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.Frontiers in genetics · 2021Review
- Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks.Scientific reports · 2020Article
- A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection.PLoS computational biology · 2020Article
- Separating Golgi Proteins fromThe Plant cell · 2019Article
- A Protocol to Map the Spatial Proteome Using HyperLOPIT inBio-protocol · 2019Article
- The subcellular organisation of Saccharomyces cerevisiae.Current opinion in chemical biology · 2019Review
- Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.Nature communications · 2019Article
- Mapping the Saccharomyces cerevisiae Spatial Proteome with High Resolution Using hyperLOPIT.Methods in molecular biology (Clifton, N.J.) · 2019Article
- A Bioconductor workflow for the Bayesian analysis of spatial proteomics.F1000Research · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.
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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.