- Human Biospecimens
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May 5, 2015
In many ways, genetic research operates through large- and small-scale processes. On the one hand, laboratory technicians examine the smallest units of genetic material to understand how diseases and medications work in the body on a molecular level. On the other hand, this microscopic work lends itself to something of an entirely different scale: Big Data. Defined as data sets that are so large or complex that traditional data processing applications are inadequate, big data in the medical field consists of all of the possible measurements and markers that we can mine through ongoing medical research.
As more and more studies capture genetic information, as well as information on disease, risk factors, demographics, and more, we are slowly building a data set that will be instrumental in future medical breakthroughs. A recent article in Forbes magazine explained how big data is critical in the personalization of healthcare, allowing us to find diagnostics and treatments that work specifically for individual patients.
Genetic Variation Requires Broad Scale Assessment
Traditionally, algorithmic analysis of large data caches has been the realm of financial institutions, technology firms, and in healthcare, mostly public health professionals. However, as genetic research has taken off, and particularly with the completion of the Human Genome Project in 2003, the value of big data in medicine has become incredibly clear. The Human Genome Project itself, which documented all three billion DNA pairs in the human genome, has already spurred more than 2,000 genetic tests for human medical conditions.
According to the University of Utah School of Medicine, while the human genome is less diverse than similar species, it is certainly not uniform. In fact, the most conservative estimate identifies genetic variation in roughly 1 out of every 1,000 DNA base pairs.
But examining and cataloging these differences on a patient-by-patient basis would not only be time-consuming, it would also only benefit the patients at hand. That is why many experts are already combining their genetic research efforts for large-scale analysis of individual data points for big data trends and results.
In addition to learning real-time from the very patients that step into physician offices every day, it is also critically important to gain access to data across time and for the longer term, which allows for testing that may not have been done at the time of patient care but is still important to understanding disease. According to the Forbes article, tumor registries and biobanks have been instrumental in the data identification and collection process.
"The old method of reactive care isn't sustainable for a cost-effective future, and it leaves too many people uncured…" according to the Forbes piece. As personalized medicine evolves, proactive big data analysis will play a significant role in helping us understand the human body so healthcare can be better targeted and more affordable for all.