J
Joe Wandy
Researcher at University of Glasgow
Publications - 26
Citations - 893
Joe Wandy is an academic researcher from University of Glasgow. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 9, co-authored 22 publications receiving 526 citations. Previous affiliations of Joe Wandy include Liverpool John Moores University & University of Manchester.
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Topic modeling for untargeted substructure exploration in metabolomics
TL;DR: An analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner is presented and it is demonstrated that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets.
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MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools
Madeleine Ernst,Madeleine Ernst,Kyo Bin Kang,Kyo Bin Kang,Andrés Mauricio Caraballo-Rodríguez,Louis-Félix Nothias,Joe Wandy,Christopher Chen,Mingxun Wang,Simon Rogers,Marnix H. Medema,Pieter C. Dorrestein,Pieter C. Dorrestein,Justin J. J. van der Hooft,Justin J. J. van der Hooft +14 more
TL;DR: MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.
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Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry.
TL;DR: Ms2lda.org is a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualizations, and the user can also decompose a data set onto predefined Mass2Motifs.
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MetAssign: Probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach
TL;DR: The Bayesian modelling introduced here combines information from the mass-to-charge ratio, retention time and intensity of each peak, together with a model of the inter-peak dependency structure, to increase the accuracy of peak annotation.
Journal ArticleDOI
Unsupervised discovery and comparison of structural families across multiple samples in untargeted metabolomics
Justin J. J. van der Hooft,Joe Wandy,Francesca Young,Sandosh Padmanabhan,Konstantinos Gerasimidis,Karl Burgess,Michael P. Barrett,Simon Rogers +7 more
TL;DR: It is concluded that by biochemical grouping of metabolites across samples MS2LDA+ aids in structural annotation of metabolites and guides prioritization of analysis by using Mass2Motif prevalence.