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Chemical 'Big Data' and Medicine

A paper just published in PLoS Computational Biology describes how information available in the NMR spectrum of blood samples can used be used to monitor the progress of patients post-kidney transplant.

The spectrum contains signals for hundreds of metabolites, the concentrations of which can be measured as they are modified by the health status of the patient, before, during and several days after organ grafting. The NMR data are complex and require sophisticated computational methods to extract medically-informative patterns.

In this work, which involved a collaboration between the clinical team of Mr Raj Prasad and Mr Paul Goldsmith at St. James' Teaching Hospital, Dr Julie Fisher and Dr Hayley Fenton (funded through the EPSRC White Rose Doctoral Training Centre for Physical Sciences at the Life Science Interface) in the School of Chemistry, and modellers Dr Sergei Krikov and Dr Emanuele Paci of the Astbury Centre for Structural Molecular Biology, it proved possible to identify patients who would subsequently go on to experience problems post-transplant, several days earlier than by conventional techniques. This is important as such information would help clinicians to decide whether helpful interventions should be made at an earlier stage leading to a better patient outcome.
This work is an illustration of the occurrence of 'Big Data' in chemistry and the need for the development of tools to handle these with the potential for huge impact.

For further information read the article at: