Challenges for Quantitative Biomedicine
Biomedical data have already attained a scale, diversity and potential that are unprecedented.
Data is the new oil. It's valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.''
Biomedical data have already attained a scale, diversity and potential that are unprecedented. Vast amounts of patient data are being generated by DNA sequencers, imagers and wearable devices, with such data here and elsewhere expected to double every 73 days by 2020. Thousands of genomes have already been sequenced in Edinburgh, exomes are being sequenced at >5 an hour worldwide, and 500 million patients worldwide are being tracked by patient monitors.
Nevertheless, large biomedical data sets need to be transformed into knowledge and hypotheses. To do so will often require team science and individuals with rare skills, each spanning traditional disciplines: a) recasting and linking data into usable forms; b) translating the jargon, tenets and assumptions of one discipline into another; and, c) identifying the optimal analytical approach and experimental design with which to answer the most important biomedical questions. A project is rarely understood deeply and completely by one researcher, and a team is formed by individuals with interlocking sets of complementary skills who can consider study limitations and opportunities which lie outside of their previous experience.
We are in a unique position to deliver on the promise of the transformative, diverse and vast data sets that are increasingly being generated - in genomics, phenomics and imaging, and within the NHS, for example. To do so we are to build a bridge between the local world-class expertise in (bio)informatics and its high-value resources (CHI numbers, population sciences, genomics and clinical genetics). Across this bridge will travel diverse skills' sets and early career researchers who will become academic, healthcare and industrial leaders, addressing important biomedical questions using complex and large data sets. These questions cannot be addressed today because of the current lack of such agile researchers of tomorrow.