With growing appreciation of the value of ontologies comes the necessary burden of managing them effectively. This presents a challenge to most life sciences organisations for several reasons.
Firstly, how do you choose the right ontology for your use case when there are usually several public and proprietary ontologies within a single domain containing overlapping or conflicting information.
Secondly, customising and updating ontologies to include bespoke terms and cope with the evolving language in new scientific fields is often only possible via overly-complicated software with a significant learning curve in order to use effectively.
Finally, many of the tools used to manage ontologies lack version control or traceability, and are not integrated with applications which actually use them.
SciBite addresses these problems with user-friendly, efficient and robust solutions which simplify collaborative ontology management and use the power of machine learning techniques to support the process of curating and enriching both internal and external ontologies.
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Databases dedicated to managing bioassay data contain an amazing wealth of R&D knowledge and, as such, provide a rich resource for mining with both scientific and operational questions. However, most pharmaceutical companies are unable to realise its true value of their data because of the way it has been captured and/or managed.
A wider scientific community initiative has resulted in the establishment of principles to ensure that data is Findable, Accessible, Interoperable and Reusable. Although initially focused on the accessibility of public domain data, the FAIR principles are rapidly gaining interest from the pharmaceutical industry.
SciBite’s unique combination of retrospective and prospective semantic enrichment immediately brings scientific intelligent search to any bioassay platform, enabling the wealth of information within it to be unlocked and exploited effectively and efficiently.
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 the 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.
The identification and application of biomarkers in basic and clinical research is almost a mandatory process in any productive pipeline of a pharmaceutical organisation. Validated biomarkers play a crucial role in the prediction of clinical outcome and support the translation from candidate discovery to successful clinical treatment.
A wealth of valuable biomarker-related information is available in the biomedical literature. However, the process of discovering and validating new biomarkers depends on the ability to extract insight from this resource effectively.
SciBite uses semantic enrichment to unlock the value of unstructured text and simplify the identification of new potential biomarker leads from scientific text.
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