91°µÍø

Dr Ruby Chang

Phd, CStat
Associate Professor of Statistics

Department: Comparative Biomedical Sciences

Campus: Camden

Research Groups: Pathogen Flow in Ecosystems, Sustainable Food Systems, Antimicrobial Resistance, Musculoskeletal Biology, Cardiovascular and Renal Biology, Brain Health and Behaviour, CPCS (Research Programme)

Research Centres: 91°µÍø Quantitative Biology Resource

Ruby is an Associate Professor of Statistics at the 91°µÍø. She provides statistical support on study design and data analysis for PG students and staff. She is a Chartered Statistician (CStat) of the Royal Statistical Society and a Fellow of the Higher Education Academy (FHEA).

Yu-Mei Ruby Chang completed her B.Sc. at Tung-hai University in Taiwan, and gained her M.Sc. degree in Statistics and Ph.D. in Animal Sciences from University of Wisconsin-Madison. Upon completing her Ph.D., she was appointed to the post of Computational Geneticist in the Dairy Science Department, University of Wisconsin-Madison, where she provided statistical and computational assistance to students and faculty staffs, and she also participated in collaborative research in the area of statistical modelling and algorithm development with application to dairy cattle improvement programs. She joined the Section of Epidemiology and Biostatics, Leeds Institute of Molecular Medicine, University of Leeds as a Research Fellow in 2006. She allied with scientists and clinicians on the genetic and epidemiological data analysis of melanoma studies. Ruby joins the 91°µÍø as a Lecturer in Statistics in 2009 (commenced as Senior Lecturer, January 2015).

Membership of Advisory/Scientific Committees and Editorial Board

  • Member of the (1/1/2018-31/12/2021; 1/1/2022-31/12/2025)
  • Member of (1/1/2022-31/12/2024)
  • Member of
  • Member of the in the Royal Statistical Society

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Ruby is interested in applying systems modelling approach to (1) understand mineral homeostasis in renal health of cats; to (2) elucidate the different connectivity and causality relationships among gait, bone shape and joint structure changes in the development of osteoarthritis. She is leading machine learning research project on characterisation and prediction of drug resistance in tuberculosis disease (collaboration with London School of Hygiene & Tropical Medicine, and Manchester Metropolitan University). She also has research interests in welfare of racehorses, and is aiming to develop deep learning methods for improved fracture detection in Thoroughbreds.

Selected Publications in Peer Reviewed Journals

I have co-authored more than 200 peer reviewed papers (including 13 first author publications) in 40+ journals. My overall citations exceed 7600 with h-index of 49 and i10-index of 126. A selection of both experimental and observational research publications in the last 7 years are listed here. For details, please check my Google Scholar page: 

  1. Rowley KJ, Townsend NB, Chang YM, Fiske-Jackson AR.2022. A computed tomographic study of endodontic and apical changes in 81 equine cheek teeth with sagittal fractures. Equine Veterinary Journal 54 (3), 541-548
  2. Knowles EJ, Elliott J, Harris PA, Chang YM, Menzies-Gow NJ. 2022. Predictors of laminitis development in a cohort of nonlaminitic ponies. Equine Veterinary Journal     
  3. Romero MP, Chang YM, Brunton LA, Parry J, Prosser A, Upton P, Drewe JA. 2022. Machine learning classification methods informing the management of inconclusive reactors at bovine tuberculosis surveillance tests in England. Preventive Veterinary Medicine v199, p105565
  4. Enache AE, Dietrich UM, Drury O, Trucco E, MacGillivray T, Syme H, Elliott J, Chang YM. 2021. Changes in retinal vascular diameters in senior and geriatric cats in association with variation in systemic blood pressure. Journal of Feline Medicine and Surgery 23 (12), 1129-1139
  5. Leopardi V, Chang YM, Pham A, Luo J, Garden OA. 2021. A systematic review of the potential implication of infectious agents in myasthenia gravis. Frontiers in neurology 12, 857
  6. Chang YM, Menges S, Westhof A, Kleinschmidt-Doerr K, Brenneis C, Pitsillides AA. 2021. Systematic analysis reveals that colony housing aligns gait profiles and strengthens link between histological and micro-CT bone markers in rat models of osteoarthritis. The FASEB Journal 35 (4), e21451
  7. Lewis RN, Chang YM, Ferguson A, Lee T, Clifforde L, Abeyesinghe SM. 2020.The effect of visitors on the behavior of zoo-housed western lowland gorillas (Gorilla gorilla gorilla). Zoo Biology 39 (5), 283-296
  8. Ter Heegde F, Luiz AP, Santana-Varela S, Magnúsdóttir R, Hopkinson M, Chang YM, Poulet B, Fowkes RC, Wood JN, Chenu C. 2020. Osteoarthritis-related nociceptive behaviour following mechanical joint loading correlates with cartilage damage. Osteoarthritis and cartilage 28 (3), 383-395
  9. Pilar Romero M, Chang YM, Brunton LA, Parry J, Prosser A, Upton P, Rees E, Tearne O, Arnold M, Stevens K, Drewe JA. 2020. Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making. Preventive Veterinary Medicine 175, 104860
  10. Sanchis-Mora S, Chang YM, Abeyesinghe SM, Fisher A, Upton N, Volk HA, Pelligand L. 2019. Pregabalin for the treatment of syringomyelia-associated neuropathic pain in dogs: A randomised, placebo-controlled, double-masked clinical trial. The Veterinary Journal 250, 55-62
  11. Goulart MR, Hlavaty SI, Chang YM, Polton G, Stell A, Perry J, Wu Y, Sharma E, Broxholme J, Lee AC, Szladovits B, Turmaine M, Gribben J, Xia D, Garden OA. 2019. Phenotypic and transcriptomic characterization of canine myeloid-derived suppressor cells. Scientific reports 9 (1), 1-14
  12. Rosanowski SM, Chang YM, Stirk AJ, Verheyen KLP. 2019. Epidemiology of race-day distal limb fracture in flat racing Thoroughbreds in Great Britain (2000–2013). Equine veterinary journal 51 (1), 83-89
  13. Carron M, Chang YM, Momanyi K, Akoko J, Kiiru J, Bettridge J, Chaloner G, Rushton J, O’Brien S, Williams N, Fevre EM, Häsler B. 2018. Campylobacter, a zoonotic pathogen of global importance: Prevalence and risk factors in the fast-evolving chicken meat system of Nairobi, Kenya. PLoS neglected tropical diseases 12 (8), e0006658
  14. Javaheri B, Poulet B, Aljazzar A, de Souza R, Piles M, Hopkinson M, Shervill E, Pollard A, Chan B, Chang YM, Orriss IR, Lee PD, Pitsillides AA. 2017. Stable sulforaphane protects against gait anomalies and modifies bone microarchitecture in the spontaneous STR/Ort model of osteoarthritis. Bone. 2017 Oct;103:308-317
  15. Borchers MR, Chang YM, Proudfoot KL, Wadsworth BA, Stone AE, Bewley JM. 2017. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. Journal of Dairy Science 100 (7), 5664-5674
  16. Fortuna L, Relf J, Chang YM, Hibbert A, Martineau HM, Garden OA. 2016. Prevalence of Foxp3+ Cells in Canine Tumours and Lymph Nodes Positively Correlates with Glucose Transporter-1 Expression. Journal of Comparative Pathology 1 (156), 114
  17. Geddes RF, Biourge V, Chang YM, Syme HM, Elliott J. 2016. The Effect of Moderate Dietary Protein and Phosphate Restriction on Calcium-Phosphate Homeostasis in Healthy Older Cats. Journal of Veterinary Internal Medicine 30 (5), 1690-1702
  18. Javaheri B, Carriero A, Staines KA, Chang YM, Houston DA, Oldknow KJ, Millan JL, Kazeruni BN, Salmon P, Shefelbine S, Farquharson C, Pitsillides AA. 2015. Phospho1 deficiency transiently modifies bone architecture yet produces consistent modification in osteocyte differentiation and vascular porosity with ageing. Bone (81) 277-291
  19. Lee KCL, Baker LA, Stanzani G, Alibhai H, Chang YM, Jimenez Palacios C, Leckie PJ, Giordano P, Priestnall SL, Antoine DJ, Jenkins RE, Goldring CE, Park BK, Andreola F, Agarwal B, Mookerjee RP, Davies NA, Jalan R. 2015. Extracorporeal liver assist device to exchange albumin and remove endotoxin in acute liver failure: Results of a pivotal pre-clinical study.  Journal of Hepatology. 63 (3) 634-642
  20. Schmitz S, Glanemann B, Garden OA, Brooks H, Chang YM, Werling D, Allenspach K. 2015. A prospective, randomized, blinded, placebo-controlled pilot study on the effect of enterococcus faecium on clinical activity and intestinal gene expression in canine food-responsive chronic enteropathy. Journal of Veterinary Internal Medicine 29 (2), 533-543 

Ruby teaches introduction to basic statistics to the BSc and MSci Biological Sciences, Bioveterinary Sciences and the MSc Wild Animal Health, Wild Animal Biology and One Health MSc courses. She also teaches introduction to basic statistics to PhD/MRes students. The PG basic statistics  training covers data summary, hypothesis testing and compare proportions, compare means of independent samples, compare means of related samples, nonparametric methods, study design and sample size, correlation and simple linear regression, and multiple regression and linear model. Recordings of all the sessions can be viewed on the Graduate School's Learn page. She also delivers both R and SPSS software training.  

In addition to the basic statistics training, Ruby also teaches advanced statistics workshops that cover (1) Introduction to Unsupervised Machine Learning, (2) Introduction to Supervised Machine Learning, (3) Introduction to Mixed Effects Model, (4) Logistics Regression and (5) Survival Analysis. Details of these training workshop can be viewed on Learn 

Ruby also provides tailored one-to-one statistics consultancy to PG students and academic/research staff.

 

 

 

  • Comparing the Movement of Crocodiles, Alligators and Caiman

    91°µÍø experts in the movement of various species have found that different crocodilian species – such as crocodiles, alligators and caiman – have very different tendencies when it come comes to galloping. Those who work closely with crocodilian species, such as in zoos and wildlife centres, had noticed over time that crocodiles, alligators and caimans move in different ways, especially when it comes to galloping – which is generally associated with horses.

  • Machine learning algorithms for predicting drug resistance against tuberculosis in people

    Tuberculosis disease (TB), caused by Mycobacterium tuberculosis, is an important global public health issue, and its drug resistance, caused by genetic mutations in the M. tuberculosis genome, poses serious challenges for effective control. Current molecular diagnostic tests are imperfect as they do not target all resistance mechanisms and drugs, nor do they inform on transmission clusters, and are therefore unable to guide completely effective individualised therapy.

  • VetCompass eClinical Trials (VETs) – Generating Interventional Evidence from Observational Data

    The study aims to develop innovative statistical approaches to veterinary electronic patient records to evaluate the effectiveness of clinical interventions in dogs.

    This project aims to develop and apply novel causal inference methods that evaluate real world interventions via routinely collected veterinary EPRs. These methods will be applied to VetCompass data to provide real world inference for some key interventions.

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