Research Thrust

Life Science Data Mining

Life sciences is a collective term encompassing biochemistry, genetics, ecology, pharmacology, medicine, and many other sciences concerned with living organisms.  The Auton Lab has diverse experience in data mining applications for these disciplines, from core areas like drug discovery and drug classification, to big-picture problems in epidemiology and pathogen detection.

Medicinal drugs are typically created through a process similar to Edison's work on the light-bulb: very smart scientists think very hard about the desired effect of a drug, then work very hard to limit their ideas to those few they can afford to carefully test.  An alternative methodology is High Throughput Screening (HTS), where truly enormous libraries of drug candidates are tested for efficacy in robotic chemistry labs.  Modern HTS labs might make only 1 mistake in 1,000 experiments, but this leads to hundreds of mistakes on a small HTS library -- roughly the same order of magnitude as the number of useful chemicals in the library.

Detecting mistakes in HTS data can save hundreds or thousands of hours of expensive wet lab time, as well as recover wrongly-disqualified candidates for further testing.  This is a job for fast, robust, and correct statistics.  When traditional statistical software packages failed to scale-up to the demands, the Auton Lab developed new algorithms that met the challenge.  Beyond the research, the Auton Lab delivered custom software libraries and user interfaces to our collaborators, to help them make use of our algorithmic innovations.