The fact that such systems provide a degree of structure shouldn’t be confused with meaning that the data managed by them isn’t messy.
Kusp provides a user-friendly, integrated solution which significantly reduces the time and cost associated with the process of data cleansing, normalisation and annotation.
Kusp ensures that your downstream integration and discovery activities are based on high quality, contextualised data.
Get in touch with the team to learn more or download the Kusp datasheet.
A single, intuitive interface to both view ontologies and annotate data, eliminating the need for error-prone copy-and-pasting between applications or websites
Specify one or more ontologies to achieve an overall desired annotation coverage
Annotate large datasets, such as human gene expression experiments, in a matter of minutes
Annotate data with high precision and create annotation rules to improve speed and accurate
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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.
For most pharmaceutical companies, extracting insight from heterogeneous and ambiguous data remains a challenge. The era of data-driven R&D is motivating investment in technologies such as machine learning to provide deeper insights into new drug development strategies.
The quality of data directly impacts the accuracy and reliability of results of computational approaches. However, the work required to achieve clean, high quality data can be costly, often prohibitively so, requiring data scientists to spend the majority of their time as ‘data janitors’, rather than actually analysing data.
SciBite provides an integrated, cost-effective solution to significantly reduce the time and cost associated with the process of data cleansing, normalisation and annotation. The output ensures that downstream integration and discovery activities are based on high quality, contextualised data.
What’s the most useful way to visualise an ontology? SciBite CTO James Malone gives his views on answering this commonly asked question regarding ontology visualisation techniques.Read
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