Please enter your details to get this resource.
Email before download
Transform business & scientific processes with semantic analytics & machine learning [Use Case]
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 use case 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.
Related articles
-
Machine Learning and phenotype triangulation
Disease detective part 3: 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 -
Are ontologies still relevant in the age of LLMs?
Technological advancements exhibit varying degrees of longevity. Some are tried and trusted, enduring longer than others, while other technologies succumb to fleeting hype without attaining substantive fruition. One constant, in this dynamic landscape is the data.
Read
How could the SciBite semantic platform help you?
Get in touch with us to find out how we can transform your data
© Copyright © 2024 Elsevier Ltd., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
