Computational approaches help to sift through and identify relevant material from multiple sources but struggle to deal with the ambiguity of scientific literature. Multiple terms can be used to describe the same topic making any keyword search difficult.
Our high-quality vocabularies and ontologies provide the critical foundation which enables SciBite’s TERMite engine to accurately detect important topics within biomedical text.
Each vocabulary is enhanced by a combination of our in-house and experienced hands-on ontologists and biocurators and our proprietary ontology enrichment software.
Our VOCabs cover many more topics in far greater depth that any publicly available ontologies such as MeSH, Uniprot and MeDDRA.
If you’re not using SciBite VOCabs, you’re not going to capture the information your users need.
Get in touch with the team to learn more or download the VOCabs datasheet.
Comprising tens of millions of synonyms, VOCabs have unrivalled coverage of all relevant terminology
VOCabs cover many more topics in far greater depth that any publicly available ontologies such as MeSH, Uniprot and MeDDRA
Each vocab is enhanced by a combination of our in-house and experienced manual curation team and our proprietary ontology enrichment software
Get in touch with us to find out how we can transform your dataContact us
Regulatory bodies expect pharmaceutical companies to maintain an up-to-date awareness of the safety implications of not only their own drugs but also those from the same drug class and with the same target that are marketed by competitors. Comprehensive, systematic monitoring is required in order to detect, validate and act upon new adverse events as early as possible.
This places significant demands on Pharmacovigilance teams, who are challenged to maintain safety and compliance amid increasingly stringent, globally diverse regulations. The legacy approach, involving manually scanning biomedical sources, is prohibitively time consuming, has a high risk of missing safety signals and is no longer a viable option.
SciBite provides a resource-effective solution to the challenges faced by Pharmacovigilance teams by unlocking the potential of unstructured biomedical content. With SciBite, pharmaceutical companies can monitor a wide range of heterogeneous and cross-disciplinary sources and reach timely, well-informed decisions, resulting in safer treatments for patients.
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.
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.
To become more information-driven, pharmaceutical companies are turning to enterprise search technologies to make faster, more informed decisions based on the most relevant information available to them. Enterprise search platforms provide the scalable, high performance infrastructure to enable secure access to millions of documents from across the whole organisation and deliver content analytics from a single portal.
However, users can typically only search for exactly what was written by the author of a document. The inconsistent use of synonyms during data entry makes it difficult to identify and collate all relevant data related to a topic of interest.
Through semantic enrichment, SciBite brings scientific understanding to enterprise search, enabling it to ‘understand’ scientific concepts within unstructured text. This opens unparalleled access to drug discovery intelligence and vast amounts of knowledge and ensures users are better informed, without overloading them with information.
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.
A closer look at our Food, Nutrients and Micronutrients VOCabRead
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