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Kusp

Automate data curation without compromising accuracy

Is most of your data managed within databases and spreadsheets?

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.

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Key product highlights

  • Unified

    Unified

    A single, intuitive interface to both view ontologies and annotate data, eliminating the need for error-prone copy-and-pasting between applications or websites

  • Flexible

    Flexible

    Specify one or more ontologies to achieve an overall desired annotation coverage

  • Fast

    Fast

    Annotate large datasets, such as human gene expression experiments, in a matter of minutes

  • Accurate

    Accurate

    Annotate data with high precision and create annotation rules to improve speed and accurate

Want to learn more about Kusp?

Get in touch with us to find out how we can transform your data

Contact us

Use cases

Transform Common Business and Scientific Processes with a Novel Combination of Semantic Analytics and Machine Learning

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.

Read the full use case

Eliminating the Data Preparation Burden

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.

Read the full use case

Related articles

  1. Exploring ontology visualisation techniques for biological 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
  2. High Performance Ontology Engineering

    One of the key aims of SciBite is to help our customers work with public ontologies in text mining applications. While these ontologies are very valuable resources, they are often built for the purpose of data organisation, not text mining.

    Read

How could the SciBite semantic platform help you?

Get in touch with us to find out how we can transform your data

Contact us