Jason Green, Ph.D
Bioinformatics in chemotherapy, radiation therapy, and drug target.
Prescribers have numerous sources of guidance about how to use drugs appropriately (e.g. dose, route, frequency, duration) for many conditions. However, this advice is based on average dose–response data derived from observations in many individuals. They can never be certain about the actual dose–response relationships for the particular patients that they treat. They can never be certain that their choice will be effective or safe for their individual patient and must recognize the need to monitor the outcome of their prescription. The inter-individual variation from the norm is often predictable and good prescribers are able to anticipate it and adjust their practice accordingly.
We use automated algorithms to perform pharmacological-related tasks which traditionally rely on human intelligence. Over the last five years, the use of our DICAT system in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Given the growing importance of Artificial Intelligence for the pharma industry, we are creating a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.
We help business executives learn what to expect from artificial intelligence in pharma. It will cover:
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