Artificial Intelligence (AI) has been touted as a way to revolutionise the entire pharmaceutical value chain. Despite such promises, tangible evidence of how AI is actually helping research has been elusive.
One of the more promising applications of AI is Machine Learning: ‘training’ a computational model to make decisions or predictions with the inclusion of a feedback loop to refine the model based on the accuracy of a given decision.
In this paper we provide a range of real-world examples that illustrate how SciBite is pioneering the use of Machine Learning and Semantic Analytics to transform common scientific and business processes, deliver robust and repeatable results and conserve the valuable time of experts.
To learn more, download the full use case.
SciBite CSO and Founder Lee Harland shares his views on how ontologies are relevant in a machine learning-centric world and are essential to help "clean up" scientific data in the Life Sciences industry.Read
In our final disease detective article, we’ll take Part 2’s topic a little further and zoom in on how we can find new relationships between diseases where direct evidence is sparse.Read
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