All our solutions are designed with one goal in mind, to help organizations navigate the complexities of life sciences by using innovative technology to make unlocking knowledge and data simple.
Whether you use one solution or a combination, from ontology management to semantic search and GenAI, they all combine to help you and your team work towards your unique objectives. Thanks to a widely compatible API-first approach that simplifies integration into your existing workflows and ecosystems, your team has the ability to curate data, enrich it, search it, analyze it and share it quickly and easily.
Explore a range of integrated user-friendly solutions that help transform years of incompatible results, records and documents into interoperable, tagged and machine-readable data that’s ready for use.
By combining our team’s world-class expertise with industry-leading software, we’re able to augment public ontologies, to deliver NER-tuned vocabularies and rapidly annotate data to power semantic searches that help you reveal vital information and key insights.
View our solutionsOur solutions are tried and tested with some of the world’s biggest organizations. We’ve developed a range of flexible and easy-to-use applications that, combined with our expertise, enable world-leading scientists to conduct research more efficiently and reach their goals faster.
Please take a look at some of the most effective ways that SciBite can help you, your team and your organization.
View use casesOver the last 13 years we’ve built up strong relationships and delivered impressive results for a range of businesses around the globe. From household names to specialist institutions, SciBite is trusted and respected by experts across our industry.
As a global leader in semantic software solutions, SciBite is trusted by many of the world’s leading scientific communities to provide expert data management and analysis to accelerate research outcomes. Hear what our partners have to say about SciBite’s capabilities.
Our collection of resources, articles and use cases showcase how SciBite delivers impressive results for all kinds of organizations and scientists around the world.
Learn more about the science behind our solutions with insight, opinion and analysis on ontologies, vocabularies, AI and LLMs, automation, data tagging, search methodology and more.
Unlock key insights with our latest in-depth featured resource.
From discovery to development: how SciBite can support your use case.
With data often being stored in various formats, this can lead to fragmentation, making it difficult to access and integrate data for meaningful insights. Knowledge graphs can help you to unify diverse datasets and show the relationship between entities to better visualize data that’s relevant to your use case. SciBite can help you build knowledge graphs in-house using the following:
SciBite’s extensive set of ontologies covers over 120 life science entities, including genes, drugs and diseases. With SciBite you have the necessary tools to extend, merge and manage these ontologies for effective graph building.
Harmonized datasetsKnowledge graphs rely on coherent data. SciBite aligns entities to single IDs captured in our ontologies to allow structured data to be cleaned and integrated, regardless of its source.
Extraction of semantic triples from textSciBite can extract semantic triples and align these entities to their ontologies. This data can then be fed into knowledge graphs to provide crucial insights.
AI is changing the way organizations view and value data. A wider scientific community initiative has resulted in the creation of FAIR principles to ensure data is Findable, Accessible, Interoperable and Reusable. Initially focused on public domain sources, FAIR data principles are gaining traction for use within the pharmaceutical industry.
Unstructured legacy data in electronic lab notebooks, proprietary databases and applications can present huge challenges to maintaining FAIR principles as there are inconsistencies in tagging, identifying and terminology. It’s often siloed and inaccessible, with queries over ownership. SciBite can help to overcome these challenges using the following resources:
CENtree creates a centralized, enterprise-ready resource through leveraging machine learning techniques, suggesting relevant synonyms and parent connections when new terms are being added.
TERMite – text analysis engineOur TERM identification, tagging and extraction engine uses named entity recognition (NER) coupled with hand-curated VOCabs to extract relevant terms found in texts and transform them into rich, machine-ready data.
Semantic searchThis function supports a number of use cases by harnessing the power of semantic analysis to rapidly scan multiple sources for relevant data, regardless of the terminology used.
Significant demands are being placed on pharmacovigilance teams who must make sure that pharmaceutical companies are compliant in maintaining awareness around the safety of drugs. Adverse event reporting is now a regulatory requirement that requires comprehensive and systematic monitoring. As a result, teams can struggle to keep up with the challenges of having few resources, and regulations, which can vary from country to country.
With hundreds of thousands of new cases added to databases every year, the challenge of processing them is exacerbated by the range of formats they are submitted in. When a manual approach is no longer practical, SciBite provides a modern and cost-effective approach to pharmacovigilance.
By using a cloud-based lake to ingest data from multiple disparate sources, SciBite’s semantic platform can use NER to identify key entries and interpret the information, with machine learning to automatically transfer and manage adverse event cases based on the necessary outcome.
Ontology management to standardize dataSciBite’s ontologies link biomedical sources focused on safety information to those that don’t to create a semantic network of interconnected facts. This means teams can find supportive information and additional insights. This is only made possible by having semantically linked rich data and specific algorithms trained to pick these up.
Predictive analysisSciBite can highlight relationships between drugs that could be missed by traditional keyword-based searches, generating new hypotheses as to when adverse events may happen.
Electronic laboratory notebooks (ELN) have become a vital source of data for pharmaceutical companies. However, much of this data is captured as free text, making it difficult to mine for insights. The inconsistent use of synonyms in data entries means that teams often miss crucial information as they struggle to identify and collate all the relevant terms for a target of interest. SciBite is here to help you to overcome these challenges.
SciBite’s sematic search can help to suggest relevant, standardized terms as you type, helping to eradicate any inconsistencies in data entries at the point of capture. Data fields are automatically semantically aware and computationally accessible, meaning data is enriched and interconnected for use across multiple disciplines.
Semantic enrichmentAs most ELNs only have very basic search capabilities, it can be difficult to find relevant data. SciBite links all relevant data regardless of which synonym or syntax is used, which can make the interrogation much simpler. With this capability, it’s also possible to conduct more complex, ontology-based data interrogation.
Explore connectionsSciBite helps researchers consider the associations between topics and methodologies by cross-linking relevant concepts that could be missed by more traditional search strategies. This means that they can gain a more holistic overview by asking questions across a range of data sources (including internal and third-party) that would usually prove time-consuming.
Identity trends and opportunitiesSciBite’s ability to cross-examine sources together with the improved visibility of ELN data allows teams to gain a better understanding of developing trends and identify areas for collaboration across companies and institutes. Alerts can be set up and managed to ensure the right information meets the right people, at the right time.
SciBite leverages large language models (LLMs), AI, and ontologies to revolutionize data retrieval. By combining vector-based search with enriched documents, we deliver improved accuracy, relevancy, and transparency. This unique approach helps organizations unlock critical insights and discover connections that traditional searches might miss, enabling researchers to find vital information faster.
SciBite’s TERMite application enriches documents with AI-powered vocabularies, making complex information machine-readable and enhancing search results with greater precision.
Improve search accuracyOntology-enriched documents ensure that scientists consistently find relevant, high-quality information, minimizing the chance of missing critical data.
Uncover hidden connectionsSciBite’s AI and ontologies go beyond exact terms, revealing connections and insights that traditional search methods often overlook.
View use caseWe build powerful partnerships with world-leading organizations.
See how we can work together to unlock new opportunities with our award-winning semantic software.
Stay up-to-date with the latest news, insights, and breakthroughs from SciBite. Explore our expert opinions, case studies, and updates on how our innovative solutions are shaping the future of scientific data management.
Access our comprehensive library of resources, including spec sheets, whitepapers, and webinars. Explore in-depth insights and expert knowledge on SciBite’s cutting-edge solutions, helping you make informed decisions and stay ahead in scientific data management.