Data standardisation enables better connectivity and improves search and discovery within an organisation.
SciBite Forms make it easy to introduce and manage terminology standards across your applications, without adding unnecessary burden to scientists.
Instantly improve your data quality and standards by semantically enriching your data at the point of capture.
Semantically aware forms ensure data is captured correctly and is computationally accessible from the outset
Components that can be easily incorporated into any web-based application to improve the user experience, including pick-lists, dynamic form elements and auto-completion to suggests relevant terms as you type
Unify all your data and provide real-time analytics during data entry to highlight areas of potential interest, such as: people who worked on this target also looked at cell type Y
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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.
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.
Most pharmaceutical companies will have, at some point, deployed an Electronic Laboratory Notebook (ELN) with the goal of centralising R&D data. ELNs have become an important source of both key experimental results and the development history of new methods and processes.
However, most pharmaceutical companies are unable to realise the true value of the data stored in their ELN. Much of the information stored within it is captured as qualitative free text or as attachments, with the ability to mine it limited to rudimentary text and keyword searches.
SciBite’s unique combination of retrospective and prospective semantic enrichment immediately brings scientifically intelligent search to any ELN platform, opening up new possibilities to mine the data more effectively and derive valuable scientific and business insights.
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