Age is the biggest risk factor for a variety of age-related diseases, such as Alzheimer’s, some types of cancer, and cardiovascular disorders. However, why and how we age still remains poorly understood. My research interests focus on understanding and defining healthy ageing and how this relates to age-related diseases using computational approaches. I use many types of data to do this, from electronic health record data to gene and protein expression datasets to help answer many different questions.
My previous research position concentrated on investigating longevity effects of chemical and pharmacological compounds on nematode worms using cheminformatics. In addition, I have worked on human and rat ageing and fitness gene expression studies using bioinformatics methods. This previous experience built on my PhD research where I utilised computational methods to predict oral absorption of pharmacological compounds.
Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption.
Newby D. et al, (2015), Eur J Med Chem, 90, 751 - 765
Comparing multilabel classification methods for provisional biopharmaceutics class prediction.
Newby D. et al, (2015), Mol Pharm, 12, 87 - 102
Pre-processing feature selection for improved C&RT models for oral absorption.
Newby D. et al, (2013), J Chem Inf Model, 53, 2730 - 2742
Coping with unbalanced class data sets in oral absorption models.
Newby D. et al, (2013), J Chem Inf Model, 53, 461 - 474
The impact of training set data distributions for modelling of passive intestinal absorption.
Ghafourian T. et al, (2012), Int J Pharm, 436, 711 - 720