Dr Jenny Wilshaw
BVetMed PhD MRCVS
Applied Data Scientist
Department: Professional Services
Campus: Camden
Jenny graduated from the аÄÃÅÁùºÏ²Ê¹ÙÍø in 2015. After graduation she worked in small animal practice, before returning to the аÄÃÅÁùºÏ²Ê¹ÙÍø to undertake a PhD in canine myxomatous mitral valve disease (MMVD). As part of her research she lead a large, international study called the HAMLET study that collected data from 2,000 dogs with MMVD & developed a model to predict which dogs could benefit from treatment. Alongside her PhD, she also completed an internship with IBM Research which evaluated drug toxicity detection using machine learning, resulting in an extended research abstract at the NeurIps Machine Learning for Health workshop.
Having received her PhD in 2021, Jenny has remained at the аÄÃÅÁùºÏ²Ê¹ÙÍø and joined the software development team to build upon her interest in the application of aritifical intelligence to clinical data.
Wilshaw J, Rosenthal SL, Wess G, Dickson D, Bevilacqua L, Dutton E, Deinert M, Abrantes R, Schneider I, Oyama MA, Gordon SG, Elliott J, Xia D, Boswood A. 2021. Accuracy of history, physical examination, cardiac biomarkers, and biochemical variables in identifying dogs with stage B2 degenerative mitral valve disease. Journal of Veterinary Internal Medicine 35 (2) 755 - 770. doi:
Wilshaw J, Stein M, Lotter N, Elliott J, Boswood A. The effect of myxomatous mitral valve disease severity on packed cell volume in dogs. 2021. Journal of Small Animal Practice 62 (6) 428 - 436. doi:
Seo J, Matthewman L, Xia D, Wilshaw J, Chang Y-M & Connolly DJ. 2020. The gut microbiome in dogs with congestive heart failure: a pilot study. Scientific Reports 10 (1): 13777. doi:
Gardiner L-J, Carrieri AP, Wilshaw J, Checkley S, Pyzer-Knapp EO & Krishna R. 2020. Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity. Scientific Reports 10 (1): 9522. doi:
Gardiner L-J, Carrieri AP, Wilshaw J, Checkley S, Pyzer-Knapp EO & Krishna R. 2019. Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development. NeurIps Machine Learning for Health - Extended Abstract. doi: