The Evolution of Search

The diagram illustrates how SciBiteSearch better addresses scientific search using Fingolimod as an example. Fingolimod (FTY720) is an FDA approved immunomodulatory drug for treating multiple sclerosis, also sold under the brand name Gilenya.

Traditional keyword search relies on a user providing all the names ("strings") by which Fingolimod is known. These are then matched to text found in the document corpus. Semantic search goes further, adding the concept of entities (things) including classes and relationships (taxonomy). Augmented search further enriches this data, enabling users to find results beyond what is inherently present in the data. Deep learning approaches are designed to handle more challenging questions and address issues such as imprecise search terms and badly indexed data.

FAIR Data Principles

The explosion of data in life sciences is leading many organisations to review their approach to data stewardship in an effort to extract maximum return from their research investment.

FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles aim to promote the integrity and re-use of scientific data. However, up to 80% of this data is stored as unstructured text such as Word documents and PDFs. At SciBite, we believe that the combination of ontologies, deep learning and FAIR data provide a powerful solution to this challenge, and our standards-based semantic tools enable FAIR data across the entire enterprise.

Advanced Search Queries

SciBiteSearch enables users to get accurate results without the need to understand the complexities of TERMite: our Named Entity Recognition (NER) engine. TERMite finds mentions of genes, diseases, drugs, companies, processes and chemicals within a document corpus. Co-occurrence matrices help users explore the resulting datasets and quickly identify relationships across thousands of related concepts.

SciBiteSearch offers powerful search capabilities with an intuitive user interface. Users can undertake basic keyword queries or employ our advanced query language to create more complex questions.

Product Highlights

SciBiteSearch offers a variety of domain-specific and generic connectors making it simple to load both open source and proprietary data. Built-in parsers also make it easy to render popular datasets and make formats searchable. Both document and sentence-level searches are supported, plus full document-level security including role-based access. SciBiteSearch has several API endpoints for integration with other key research systems.

SciBiteSearch allows organisations to incorporate their own branding via UI themes, colour schemes and logos. Personalisation settings provide tailored support for expert users and those who require less sophisticated functionality.

The Way Ahead

The SciBiteSearch development roadmap will focus on several key areas with the objective of becoming the best biomedical research solution for your department or small enterprise.

Our plans include providing seamless access to additional insights, answers and content sources together with more augmented search, question-answering capabilities and connectors for both structured and unstructured data sources. We will also have deeper integration with SciBiteAI models and SciBite’s CENtree ontology management solution so that customers can leverage our full ecosystem of tools.

Leverage our Experience of Semantics for Intelligent Scientific Search

At SciBite we have experience in developing and deploying semantic deep learning models that perform a wide variety of functions:-

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Customer Use Cases