In early 2013, SynapDx, an early stage laboratory services company, started clinical trials of a new blood-based test to enable earlier detection of Autism Spectrum Disorders (ASDs). The test is reported to have an estimated 85% accuracy rate, attracting the interest of Google Ventures. This investment arm of the search giant partnered with SynapDx to support the exploration of the “growing body of data that could link to genetics-based solutions.”
Researchers, analysts, and practitioners are realizing that implementing data analytics methods can not only help teams answer specific questions, but also provide meaningful insights to drive discovery research, enrich planning, and enable decision making. SynapDx’s clinical trials offer a new avenue for applying data analytics in biomedical research, particularly disease genomics – a topic Praescient covered previously when studying how to apply methods of spreader networks to combat disease.
Bridging data analytics methods between industries and applying methodologies from proven projects can spark creativity, increase collaboration, and potentially accelerate solutions as we all strive to understand the complex condition of autism. Praescient has proven the ability to cross pollinate these tools and techniques by leveraging workflows first developed in the national security community to help investment banks analyze fraudulent behavior and activity. SynapDx, and other similar organizations, would benefit from implementing data analytics capabilities to understand the “diverse genetic roots” of Autism Spectrum Disorder, and possibly generate insights for studying disease more broadly.
As Isaac Kohane, a pediatric endocrinologist and computer scientist at Children’s Hospital Boston, notes about cancer research, “There may be hundreds of different molecularly defined cancers, which each have their own specific optimal treatment.” Comparing these conclusions to the SynapDx trials, we have already seen how molecules appear clinically in slightly different ways. SynapDx has traced 489 genes and narrowed that list down to 55 genes that could predict autism in about two-thirds of those with the disease. It is here that applying network analysis methods would enable researchers and practitioners to examine both how many roots exist and how the roots are connected within the given data sets.
Data analytics solutions provide a robust approach for managing the growing volume of information collected from clinical trials. These methodologies serve to streamline organizational efficiencies and provide oversight to maximize investment through empowered decision making practices. The success achieved on one project holds the promise to potentially bridge capabilities and share insights across the larger life sciences community, advancing all efforts to make a difference in the way we live and work.