Computational Biology is a broad, interdisciplinary field that has grown over the last 20 years to become central to biomedical discovery and development in many health-related disciplines. The development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological data is producing profound results.
Quantitative Medicine is exclusively licensing, patent pending active machine learning technology developed at the Ray and Stephanie Lane Center for Computational Biology at Carnegie Mellon University. Two of the Company’s founders, Professor Robert F. Murphy PhD, Director of the Lane Center, and Joshua Kangas PhD, Quantitative Medicine’s Chief Science Officer, patented the active machine learning technology in the Computational Research Engine™ (CoRE™). This CoRE™ technology employs cutting-edge computational biology science to transform the drug discovery and development process.
The CoRE™ utilizes a variety of sophisticated computational biology techniques for accessing and manipulating all of the different kinds of data needed in drug discovery. Once accessed, the CoRE™’s predictive analytics, developed by our computational biologists, can use this data to support a number of steps in the drug discovery and development process, as seen below.
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Examples of the data to be utilized in the research supported by Quantitative Medicine’s Computational Research Engine™, CoRE™, includes:
- genomics data
- target similarity
- target biology
- screening data