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Professor David Broadhurst


Staff Member Details
Telephone: +61 8 6304 2705
Mobile: 0403 707 305
Campus: Joondalup  
Room: JO19.332  

David is the Professor of Data Science & Biostatistics in the School of Science, and director of the Centre for Integrative Metabolomics & Computational Biology (CiMCB).


David holds a first class honours degree in Electronic Engineering, an MSc in Medical Informatics, and a PhD in the ‘‘Application of Artificial Neural Networks and Evolutionary Algorithms to Metabolic Profiling’’. He has been an active member of the Systems Biology community for over 20 years, where he has recognised expertise in design of experiments, signal processing, biostatistics, and machine learning. He started his professional career at University of Wales, Aberystwyth, witnessing the birth of Metabolomics (the quantitative study of low-molecular weight chemicals in a biological system related to genetic or environmental perturbations) as an independent post-genomic science, under the mentorship of Professor Douglas Kell C.B.E. After leaving Wales, David worked for an extended period as a post-doctoral research fellow at the University of Manchester developing large-scale clinical metabolomics protocols in collaboration with GSK and AstraZeneca. In 2009 he moved to Cork University Maternity Hospital, Ireland, to work as part of a team investigating pre-symptomatic metabolite biomarkers predictive of major pregnancy disorders. In 2011 David was appointed Associate Professor of Biostatistics at the University of Alberta, Canada, where he was scientific lead for a range of basic/clinical metabolomics projects, and continued his pregnancy related research. More recently he has expanded his research portfolio to a diverse range of post-genomic translational/precision medicine projects. In March 2016 he was appointed to his current position as Professor at Edith Cowan University. David’s current research focuses on integrative metabolomics and computational biology. With particular emphasis on multi-system/multi-omic data integration, population stratification via personalised trajectories, model optimization by evolutionary computation, artificial neural networks, and data visualisation.

Professional Associations

Director of the International Metabolomics Society.


  • Doctor of Philosophy, Wales, 1998.
  • Master of Science, England, 1993.
  • Bachelor of Engineering, England, 1992.


Recent Research Grants

  • An exploratory study to determine if exercise can impact the gut microbiota composition of men receiving androgen suppression therapy for prostate cancer,  Prostate Cancer Foundation of Australia,  Grant,  2018 - 2023,  $98,875.
  • Securing health in Fiji through strengthened health systems and integrated water management to tackle the Three Plagues: typhoid, dengue and leptospirosis.,  National Health and Medical Research Council,  DFAT - Stronger Systems For Health Security Call,  2018 - 2022,  $375,179.
  • Multi-centre, multi-disciplinary study using a systems biology approach to investigate immunomodulation in children with acute wheeze,  National Health and Medical Research Council,  Project Grants,  2018 - 2021,  $73,873.
  • Australian Metabolic Phenotyping Centre (AMPC),  Australian Research Council,  Grant - Linkage (Infrastructure),  2017 - 2019,  $33,130.
  • High Throughput NMR Facility,  Australian Research Council,  Grant - Linkage (Infrastructure),  2019,  $415,000.
  • Investigating lipid synthesis pathways in genetically modified seeds,  Edith Cowan University,  Edith Cowan University, School of Science Early Career Grant Scheme Semester 1, 2017,  2017,  $10,000.
  • Improving barley malting quality through a combined metabolomics and transcriptomics approach,  Edith Cowan University,  Edith Cowan University, School of Science Early Career Grant Scheme Semester 1, 2017,  2017,  $10,000.

Recent Publications (within the last five years)

Journal Articles

  • Mendez, K., Broadhurst, D., Reinke, S., (2020), Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks. Metabolomics, 16(2), Article number 17, DOI:
  • Lette, E., Lawler, N., Burnham, Q., Boyce, M., Duffy, R., Koenders, A., Broadhurst, D., (2020), Metabolomic Profiling of Crayfish Haemolymph Distinguishes Sister Species and Sex: Implications for Conservation, Aquaculture and Physiological Studies. Freshwater Crayfish, 25(1), 89-101, DOI:
  • Mendez, K., Pritchard, L., Reinke, S., Broadhurst, D., (2019), Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing. Metabolomics, 15(10), Article no.125, DOI: 10.1007/s11306-019-1588-0.
  • Mendez, K., Broadhurst, D., Reinke, S., (2019), The application of artificial neural networks in metabolomics: a historical perspective. Metabolomics, 15(11), Article Number 142, DOI: 10.1007/s11306-019-1608-0.
  • Newton, R., Christophersen, C., Fairman, C., Hart, N., Taaffe, D., Broadhurst, D., Devine, A., Chee, R., Tang, C., Spry, N., Galvao, D., (2019), Does exercise impact gut microbiota composition in men receiving androgen deprivation therapy for prostate cancer? A single-blinded, two-armed, randomised controlled trial. BMJ Open, 9(4), article no. e024872, DOI: 10.1136/bmjopen-2018-024872.
  • Beger, R., Dunn, W., Bandukwala, A., Bethan, B., Broadhurst, D., Clish, C., Dasari, S., Derr, L., Evans, A., Fischer, S., Flynn, T., Hartung, T., Herrington, D., Higashi, R., Hsu, P., Jones, C., Kachman, M., Karuso, H., Kruppa, G., Lippa, K., Maruvada, P., Mosley, J., Ntai, I., O’donovan, C., Playdon, M., Raftery, D., Shaughnessy, D., Souza, A., Spaeder, T., Spalholz, B., Tayyari, F., Ubhi, B., Verma, M., Walk, T., Wilson, I., Witkin, K., Bearden, D., Zanetti, K., (2019), Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics, 15(1), article number 4, DOI: 10.1007/s11306-018-1460-7.
  • Mendez, K., Reinke, S., Broadhurst, D., (2019), A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification. Metabolomics, 15(12), Article number: 150, DOI:
  • Lawler, N., Abbiss, C., Gummer, J., Broadhurst, D., Govus, A., Fairchild, T., Thompson, K., Garvican-Lewis, L., Gore, C., Maker, G., Trengove, R., Peiffer, J., (2019), Characterizing the plasma metabolome during 14 days of live-high, train-low simulated altitude: A metabolomic approach. Experimental Physiology, 104(1), 81-92, United Kingdom, Wiley-Blackwell Publishing Ltd., DOI: 10.1113/EP087159.
  • Denihan, NM., Kirwan, JA., Walsh, BH., Dunn, WB., Broadhurst, D., Boylan, GB., Murray, DM., (2019), Untargeted metabolomic analysis and pathway discovery in perinatal asphyxia and hypoxic-ischaemic encephalopathy. Journal of Cerebral Blood Flow and Metabolism, 39(1), 147-162, DOI: 10.1177/0271678X17726502.
  • Broadhurst, D., Goodacre, R., Reinke, S., Kuligowski, J., Wilson, I., Lewis, M., Dunn, W., (2018), Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14(6), article no.72, DOI: 10.1007/s11306-018-1367-3.
  • Manaf, F., Lawler, N., Peiffer, J., Maker, G., Boyce, M., Fairchild, T., Broadhurst, D., (2018), Characterizing the plasma metabolome during and following a maximal exercise cycling test. Journal of Applied Physiology, 125(4), 1193-1203, Bethesda, USA, American Physiological Society, DOI: 10.1152/japplphysiol.00499.2018.
  • Cassiède, M., Mercier, P., Shipley, P., Dueck, M., Kamravaei, S., Nair, S., Mino, J., Pei, L., Broadhurst, D., Lacy, P., Quémerais, B., (2018), Comparison of computational approaches for identification and quantification of urinary metabolites in 1H NMR spectra. Analytical Methods: advancing methods and applications, 10(18), 2129-2137, Royal Society of Chemistry, DOI: 10.1039/c8ay00830b.
  • Kirwan, J., Brennan, L., Broadhurst, D., Fiehn, O., Cascante, M., Dunn, W., Schmidt, M., Velagapudi, V., (2018), Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for “Precision medicine and pharmacometabolomics task group”—The metabolomics society initiative). Clinical Chemistry (Washington, DC): international journal of molecular diagnostics and laboratory medicine, 64(8), 1158-1182, American Association for Clinical Chemistry Inc., DOI: 10.1373/clinchem.2018.287045.
  • Reinke, S., Galindo-Prieto, B., Skotare, T., Broadhurst, D., Singhania, A., Horowitz, D., Djukanovic, R., Hinks, T., Geladi, P., Trygg, J., Wheelock, C., (2018), OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma. Analytical Chemistry, 90(22), 13400-13408, DOI: 10.1021/acs.analchem.8b03205.
  • Dunn, W., Broadhurst, D., Edison, A., Guillou, C., Viant, M., Bearden, D., Beger, R., (2017), Quality assurance and quality control processes: summary of a metabolomics community questionnaire. Metabolomics, 13(5), article no.50, DOI: 10.1007/s11306-017-1188-9.
  • Murphy, N., Khashan, A., Broadhurst, D., Gilligan, O., O''donoghue, K., Kenny, L., (2016), Perinatal determinants of D-dimer levels in a cross-sectional study of low risk pregnant women. Obstetric Medicine, 9(2), 78-82, SAGE Publications Inc., DOI: 10.1177/1753495X15625547.
  • Beger, RD., Dunn, W., Schmidt, M., Gross, S., Kirwan, J., Cascante, M., Brennan, L., Wishart, D., Oresic, M., Hankemeier, T., Broadhurst, D., Lane, A., Suhre, K., Kastenmuller, G., Sumner, S., Thiele, I., Fiehn, O., Kaddurah-Daouk, R., (2016), Metabolomics enables precision medicine: ‘‘A White Paper, Community Perspective’’. Metabolomics, 12(10), Article number 149, New York, USA, Springer, DOI: 10.1007/s11306-016-1094-6.
  • Ahearne, C., Denihan, N., Walsh, B., Reinke, S., Kenny, L., Boylan, G., Broadhurst, D., Murray, D., (2016), Early Cord Metabolite Index and Outcome in Perinatal Asphyxia and Hypoxic-Ischaemic Encephalopathy. Neonatology: foetal and neonatal research, 110(4), 296-302, S Karger AG, DOI: 10.1159/000446556.
  • Landi, A., Broadhurst, D., Vernon, S., Tyrrell, DL., Houghton, M., (2016), Reductions in circulating levels of IL-16, IL-7 and VEGF-A in myalgic encephalomyelitis/chronic fatigue syndrome. Cytokine, 78(2016), 27-36, Academic Press, DOI: 10.1016/j.cyto.2015.11.018.
  • Currie, F., Broadhurst, D., Dunn, W., Sellick, C., Goodacre, R., (2016), Metabolomics reveals the physiological response of Pseudomonas putida KT2440 (UWC1) after pharmaceutical exposure. Molecular BioSystems, 12(4), 1367-1377, Royal Society of Chemistry, DOI: 10.1039/c5mb00889a.
  • Chan, A., Mercier, P., Schiller, D., Bailey, R., Robbins, S., Eurich, D., Sawyer, M., Broadhurst, D., (2016), 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer. British Journal of Cancer, 114(1), 59-62, London, UK, Nature Publishing Group, DOI: 10.1038/bjc.2015.414.

Research Student Supervision

Principal Supervisor

  • Master of Science (Chemistry),  Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data

Associate Supervisor

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