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Shifting from Insight to Foresight: Welcome to the New Life Sciences Paradigm
With rising healthcare prices and growing populations, the life sciences industry is under increasing pressure to innovate faster. Better diagnostics and emerging data analytics technologies promise to make this possible, with the goal of predicting outcomes in an automated, systematic manner to answer questions with pinpoint accuracy. For example:
- Would a novel molecule have better biological activity or availability than the current lead molecule?
- How would a phenotype change with a modulation within a pathway, or even which node is best to target?
- Which patients have the highest likelihood of responding to a drug in a clinical trial?
- Where can a drug have the most impact in improving the overall health of the human population?
The more data is available, the better the industry can simulate processes in a consistent and logical way, and the faster and cheaper a drug can get to the patient. This white paper explores what needs to happen for life sciences organizations to transition from an insight-driven approach where experimental outcomes are interpreted to a foresight-driven approach where the answer can be predicated before performing experiment design and execution.
Download the white paper for EPAM’s latest thinking on the strategies and technologies that can support this shift to a predictive, foresight-driven R&D paradigm for life sciences.
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Blog
The Promise of Analytics in Life Sciences
Follow along with EPAM’s Chief Scientist, Chris Waller, as he relates using analytics to solve for a disease state to putting together a jigsaw puzzle.
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Blog
The Practice of Analytics in Life Sciences
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Blog
Mapping the Analytics Continuum in Life Sciences
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Blog
Genomics in the World of Big Data Analytics
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Press Release
EPAM Wins 2019 Big Innovation Award for TelescopeAI™
By leveraging technologies like artificial intelligence, machine learning and predictive analytics, TelescopeAI helps distributed technology teams unlock valuable IT insights.