Steve O'Hagan
Steve O'Hagan
Computer Officer, MIB & School of Chemistry, University of Manchester
Verified email at - Homepage
Cited by
Cited by
Development of a robust and repeatable UPLC− MS method for the long-term metabolomic study of human serum
E Zelena, WB Dunn, D Broadhurst, S Francis-McIntyre, KM Carroll, ...
Analytical chemistry 81 (4), 1357-1364, 2009
Molecular phenotyping of a UK population: defining the human serum metabolome
WB Dunn, W Lin, D Broadhurst, P Begley, M Brown, E Zelena, ...
Metabolomics 11, 9-26, 2015
Illuminating disease and enlightening biomedicine: Raman spectroscopy as a diagnostic tool
DI Ellis, DP Cowcher, L Ashton, S O'Hagan, R Goodacre
Analyst 138 (14), 3871-3884, 2013
A metabolome pipeline: from concept to data to knowledge
M Brown, WB Dunn, DI Ellis, R Goodacre, J Handl, JD Knowles, ...
Metabolomics 1, 39-51, 2005
Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast …
S O'Hagan, WB Dunn, M Brown, JD Knowles, DB Kell
Analytical Chemistry 77 (1), 290-303, 2005
COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access
RM Salek, S Neumann, D Schober, J Hummel, K Billiau, J Kopka, ...
Metabolomics 11, 1587-1597, 2015
Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics
S O'Hagan, WB Dunn, JD Knowles, D Broadhurst, R Williams, ...
Analytical Chemistry 79 (2), 464-476, 2007
A ‘rule of 0.5’for the metabolite-likeness of approved pharmaceutical drugs
S O′ Hagan, N Swainston, J Handl, DB Kell
Metabolomics 11, 323-339, 2015
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Y Khemchandani, S O’Hagan, S Samanta, N Swainston, TJ Roberts, ...
Journal of cheminformatics 12, 1-17, 2020
GeneGini: Assessment via the Gini coefficient of reference “housekeeping” genes and diverse human transporter expression profiles
S O'Hagan, MW Muelas, PJ Day, E Lundberg, DB Kell
Cell systems 6 (2), 230-244. e1, 2018
The apparent permeabilities of Caco-2 cells to marketed drugs: magnitude, and independence from both biophysical properties and endogenite similarities
S O’Hagan, DB Kell
PeerJ 3, e1405, 2015
A brain-permeable inhibitor of the neurodegenerative disease target kynurenine 3-monooxygenase prevents accumulation of neurotoxic metabolites
S Zhang, M Sakuma, GS Deora, CW Levy, A Klausing, C Breda, KD Read, ...
Communications biology 2 (1), 271, 2019
The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data
M Wright Muelas, F Mughal, S O’Hagan, PJ Day, DB Kell
Scientific reports 9 (1), 17960, 2019
VAE-Sim: a novel molecular similarity measure based on a variational autoencoder
S Samanta, S O’Hagan, N Swainston, TJ Roberts, DB Kell
Molecules 25 (15), 3446, 2020
Consensus rank orderings of molecular fingerprints illustrate the ‘most genuine’similarities between marketed drugs and small endogenous human metabolites, but highlight …
S O’Hagan, DB Kell
ADMET and DMPK 5 (2), 85-125, 2017
Software review: the KNIME workflow environment and its applications in Genetic Programming and machine learning
S O’Hagan, DB Kell
Genetic Programming and Evolvable Machines 16, 387-391, 2015
Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites
S O'Hagan, DB Kell
Frontiers in pharmacology 6, 139770, 2015
An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines
M Wright Muelas, I Roberts, F Mughal, S O’Hagan, PJ Day, DB Kell
Metabolomics 16, 1-12, 2020
Analysing and navigating natural products space for generating small, diverse, but representative chemical libraries
S O’Hagan, DB Kell
Biotechnology Journal 13 (1), 1700503, 2018
Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures
S O’hagan, DB Kell
Journal of Cheminformatics 9 (1), 18, 2017
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