Machine Learning and Big Data in Precision Medicine

Healthcare data is predicted to expand by 43 percent by 2020, to an incomprehensible level of 2.3 zettabytes. The size of the data is also not the only inevitable issue, it’s the type of data. Eighty percent of it is completely unstructured and mostly unlabelled, meaning organizations will find it increasingly difficult to extract any value or outcomes from the datasets [1].

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Pluripotent Potential for Clinical Application

Since their development in the mid-2000s, the versatile nature of induced Pluripotent Stem Cells (iPSCs) has unlocked the potential of curative approaches instead of symptom-reactive treatments. In particular, fields which deal with genetic disorders and regenerative therapies would benefit from this. The advancement of iPSC technology compliments the advent of personalized medicine, allowing for a future where individuals could be treated using autologous iPSCs.

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An Improved Model for IBD Drug Discovery

Animal models have been used for years to provide proof of concept for new therapies, however there are major flaws which need to be addressed. Studies using mouse models cannot accurately predict patient response to a new compound. Translatability requires a suitable model which will reduce attrition rates in phase II and III clinical trials which are proving to be of detriment to R&D productivity[1].

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Application of Pharmacogenomics and Bioinformatics to Exemplify the Utility of Human ex vivo Organoculture Models in the Field of Precision Medicine

It is well recognised that one size does not fit all when it comes to the treatment of many diseases. Getting the right drug to the right patient at the right dose has become the focus of precision medicine, which provides hope that patients may receive the most appropriate treatment sooner, improving their quality of life and reducing the support required from health care systems and wider society[1]. Health economists are recognising the potential of precision medicine and are beginning to apply the concept to their research[2].

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