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Dr. Belinda Hernández

Dr. Belinda Hernández is a Senior Research Fellow with TILDA. Her research interests include Bayesian models and machine learning methods for use on high dimensional data such as proteomic and genomic data as well as multivariate and predictive modelling. She works across a number of teams in TILDA such as the Neuro Cognitive, Neuro Cardiovascular, Frailty and Resilience and the Bioengineering groups.

Prior to TILDA Belinda was an Assistant Professor in Statistics in the School of Mathematics and Statistics UCD (2015-2017) and completed her PhD in Bayesian methods for Proteomic Biomarker development and validation in UCD in 2015. Belinda also has an MSc in Applied Statistics from Oxford University (first class honours) and a BA Mod in Management Science and Information Systems from Trinity College Dublin (first class honours).


  • Hernández B, Setti A, Kenny RA, Newell FN, Individual differences in ageing, cognitive status, and sex on susceptibility to the sound-induced flash illusion: A large-scale study (2019) Psychology and Aging, 37(7) 978

  • Hernández B, Reilly RB, Kenny RA, Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules (2019) Scientific reports 9 (1), 1-12

  • BA McGivney BA, Hernández B, Katz LM, MacHugh DE, McGovern SP, Parnell AC, Wiencko HL, Hill EW, A genomic prediction model for racecourse starts in the Thoroughbred horse, Animal genetics 50 (4), 347-357

  • Gohar F, McArdle A, Jones M, Callan N, Hernández B, Kessel C, Miranda-Garcia M, Lavric M, Holzinger D, Pretzer C, Lainka E, Vastert SJ, de Roock S, FitzGerald O, Pennington SR, Foell D, Molecular signature characterisation of different inflammatory phenotypes of systemic juvenile idiopathic arthritis, (2019) Annals of the rheumatic diseases 78 (8), 1107-1113

  • Oliveira IM, Hernández B, Kenny RA, Reilly RB (2019), Automatic Disability Categorisation based on ADLs among Older Adults in a Nationally Representative Population using Data Mining Methods, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2466-2469

  • Whelan D, Delahunt E, O'Reilly M, Hernández B, Caulfield B,Determining Interrater and Intrarater Levels of Agreement in Students and Clinicians When Visually Evaluating Movement Proficiency During Screening Assessments (2019), Physical therapy 99 (4), 478-486

  • Hernández B, Raftery A.E., Pennington S.R. and Parnell A.C., 2016, BART-BMA Bayesian Additive Regression Trees using Bayesian Model Averaging, 2017, Statistics and Computing, pp1-22

  • Rue-Albrecht K, McGettigan,P.A.,Hernández B, Parnell A.C., Gordon S.V. and MacHugh D.E., GOexpress: Visualise microarray and RNAseq data using gene ontology annotations, 2016, BMC Bioinformatics, 17 (1), pp 1

  • Hernández B, Parnell A.C. and Pennington S.R., Bayesian methods for the discovery of proteomic biomarkers, 2015, EuPA open Proteomics, 9, pp 54-64

  • Hernández B, Parnell A.C., Pennington S.R., Why have so few proteomic biomarkers ”survived” validation? (Sample size and independent validation considerations),2014, Proteomics, 14 (13-14), pp 1587-1592

  • Ademowo OS, Collins E, Hernández, B, Rooney C, van Kuijk A, Gerlag D, Tak PP, Fearon U, FitzGerald O and Pennington SR, Discovery and Confirmation of a Protein Biomarker Panel which Predicts Response to anti-TNF-alpha (Adalimumab) Therapy in Psoriatic Arthritis, Annals of Rheumatic Diseases, 2015, 75(1), pp 234-241

  • Ramm S, Morissey B, Hernández B, Rooney C, Pennington SR and Mally A, Application of proteomics to identify deregulated proteins associated with idiosyncratic liver toxicity in a rat model of LPS/Diclofenac co-administration, 2015, Journal of Toxicology, 331, pp 100-111

  • Hernández B, Lipkovich,I and O’Kelly M, 2014, Chapter 8:Doubly Robust Missing Data in Clinical Trials in Clinical Trials with Missing Data: A Guide for Practitioners, pp 370-407, Wiely, London

  • Staunton L, Clancy T, Tonry C, Hernández B, Ademowo S, Dharsee M, Evans K, Parnell AC, Tasken K, Watson W and Pennington S.R., 2014, Chapter 13:Protein Quantification by MRM for Biomarker Validation in New Developments in Mass Spectrometry, Quantatative Proteomics, pp 279-315, Royal Society Chemistry


  • 2018 Finalist European Data Science and AI Awards

  • 2017 UCD Venture Launch Start Up of the Year Award