Machine Learning for Personalized Medicine

Translation and personalised medicine: Genome medicine meets reality

by Winston Hide

Genomics offers resolution of each individuals’ genome. Rare and common variants found within the genome offer the tantalising potential that these can become informative markers predictive of wellness and susceptibility to disease. But for most common diseases a significant proportion of the genetic contribution to disease susceptibility remains to be discovered. Genetics can be quantified and so offers a realistic front end to determining a patient’s profiles of susceptibility. But there are several missing components if we are going to truly resolve appropriate treatment at the individual level. Genomes code for function and it is variation in functional profiles are the active contributors to phenotypes. If we are to make progress in personalised medicine we must have a quantifiable handle upon the relationship between genetics and functional activity. This talk will explore novel and existing approaches to genome translation. It will explore insights into the concept that translational genomic research is an essential prerequisite for developing sound, evidence-based diagnostic, therapeutic and prognostic clinical protocols for the practice of personalised clinical medicine.

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