I supervise the AI work of our laboratory, which consists of neural networks (NNs) applied to genetics, transcriptomics, proteomics, iPSC cells microscopy, brain imaging, and text from Electronic Health Records. We apply these to data coming from biotech laboratories (i.e. genomics, transcriptomics, proteomics and metabolomics from human samples and iPSC cells) and hospitals or GP practices (i.e. Electronic Health Records and cohorts of volunteer patients). In the case of biotech data, bioinformatics methods allow us to know which are the metabolic processes associated with neurological diseases, such that appropriate pharmaceutical targets and drugs can be developed. In the case of hospital and GP data, artificial intelligence and neural networks allow us to extract from the text notes written by doctors what are the diagnoses, medications, symptoms and medical test results of millions of patients, which in turn can be analysed to evaluate the most efficient treatment per patient (personalised medicine), or whether certain drugs are serendipitously ameliorating psychiatric conditions (drug repurposing). Often, we combine biotech and hospital/GP data to validate laboratory results with real world evidence, such that any target or treatments we propose increases its chances of succeeding in the final stages of clinical trials. In all cases, high performance computing is the software (e.g. concurrent programming, threading or CUDA) and hardware (e.g. our 70 GPUs computational cluster plus cloud facilities) of choice to perform all these calculations in hours rather than years.

In summary, we believe in the benefits that information technologies can bring (and are bringing) to health care and drug discovery, and we actively work towards implementing these methods in the lab and the clinic, and on developing the very computational methods that make this possible.