Maria De Iorio
Maria De Iorio
Professor of Biostatistics, National University of Singapore
Verified email at
Cited by
Cited by
Human metabolic phenotype diversity and its association with diet and blood pressure
E Holmes, RL Loo, J Stamler, M Bictash, IKS Yap, Q Chan, T Ebbels, ...
Nature 453 (7193), 396-400, 2008
An ANOVA model for dependent random measures
M De Iorio, P Müller, GL Rosner, SN MacEachern
Journal of the American Statistical Association 99 (465), 205-215, 2004
Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies
CJ Hoggart, JC Whittaker, M De Iorio, DJ Balding
PLoS genetics 4 (7), e1000130, 2008
Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events
M Pavlou, G Ambler, S Seaman, M De Iorio, RZ Omar
Statistics in medicine 35 (7), 1159-1177, 2016
Genome‐wide significance for dense SNP and resequencing data
CJ Hoggart, TG Clark, M De Iorio, JC Whittaker, DJ Balding
Genetic Epidemiology: The Official Publication of the International Genetic …, 2008
Bayesian nonparametric nonproportional hazards survival modeling
M De Iorio, WO Johnson, P Müller, GL Rosner
Biometrics 65 (3), 762-771, 2009
Optimal Bayesian design by inhomogeneous Markov chain simulation
P Müller, B Sansó, M De Iorio
Journal of the American Statistical Association 99 (467), 788-798, 2004
Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN
J Hao, M Liebeke, W Astle, M De Iorio, JG Bundy, TMD Ebbels
Nature protocols 9 (6), 1416-1427, 2014
BATMAN—an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model
J Hao, W Astle, M De Iorio, TMD Ebbels
Bioinformatics 28 (15), 2088-2090, 2012
Opening up the" Black Box": Metabolic phenotyping and metabolome-wide association studies in epidemiology
M Bictash, TM Ebbels, Q Chan, RL Loo, IKS Yap, IJ Brown, M De Iorio, ...
Journal of clinical epidemiology 63 (9), 970-979, 2010
Meeting-in-the-middle using metabolic profiling–a strategy for the identification of intermediate biomarkers in cohort studies
M Chadeau-Hyam, TJ Athersuch, HC Keun, M De Iorio, TMD Ebbels, ...
Biomarkers 16 (1), 83-88, 2011
Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification
M Chadeau-Hyam, TMD Ebbels, IJ Brown, Q Chan, J Stamler, CC Huang, ...
Journal of proteome research 9 (9), 4620-4627, 2010
Significance testing in ridge regression for genetic data
E Cule, P Vineis, M De Iorio
BMC bioinformatics 12, 1-15, 2011
Sequence-level population simulations over large genomic regions
CJ Hoggart, M Chadeau-Hyam, TG Clark, R Lampariello, JC Whittaker, ...
Genetics 177 (3), 1725-1731, 2007
Metabolome-wide association study identifies multiple biomarkers that discriminate north and south Chinese populations at differing risks of cardiovascular disease: INTERMAP study
IKS Yap, IJ Brown, Q Chan, A Wijeyesekera, I Garcia-Perez, M Bictash, ...
Journal of proteome research 9 (12), 6647-6654, 2010
Ridge regression in prediction problems: automatic choice of the ridge parameter
E Cule, M De Iorio
Genetic epidemiology 37 (7), 704-714, 2013
Importance sampling on coalescent histories. I
M De Iorio, RC Griffiths
Advances in Applied Probability 36 (2), 417-433, 2004
Conserved Mosquito/Parasite Interactions Affect Development of Plasmodium falciparum in Africa
AM Mendes, T Schlegelmilch, A Cohuet, P Awono-Ambene, M De Iorio, ...
PLoS pathogens 4 (5), e1000069, 2008
Metabolic profiling of polycystic ovary syndrome reveals interactions with abdominal obesity
A Couto Alves, B Valcarcel, VP Mäkinen, L Morin-Papunen, S Sebert, ...
International Journal of Obesity 41 (9), 1331-1340, 2017
Importance sampling on coalescent histories. II: Subdivided population models
M De Iorio, RC Griffiths
Advances in Applied Probability 36 (2), 434-454, 2004
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