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Data cleansing to unlock the potential of bioassay data
[Use case]

Ontology management

The Business challenge:

A global pharmaceutical company recognized the potential of the huge volumes of bioassay data that they had generated but struggled to gain insights from this valuable resource. A lack of standardization across their data repositories, including LIMS and other bioassay databases, had resulted in different ways to describe the same thing, for example, ‘mouse’, ’mice’, ‘Mus musculus’ and ‘m. musculus’, making it hard to collate data for a particular species. This was compounded by the fact that some database fields were sparsely populated fields while others contained useful information buried in long assay descriptions.

The SciBite solution:

We enriched our species, gene, and bioassay vocabularies with customer-specific terms and synonyms to ensure all relevant information would be recognized. We then analysed the assay names from the legacy database and extracted the different entities within each one. Each entity was extracted and mapped to a single, standard vocabulary term to normalize the data.

Extraction of Cell Line, Drug, Species and Target entities within the unstructured titles of a selection of assays

Figure: Extraction of Cell Line, Drug, Species and Target entities within the unstructured titles of a selection of assays. The resulting semantic index enables connections to be made between bioassays

Key business benefits:

  • Assays are consistently and unambiguously tagged with key metadata
  • Enables the wealth of information in bioassay databases to be unlocked and exploited

Find out more about how our Ontology Services can benefit your business.

Learn more


Related articles

  1. Are ontologies relevant in a machine learning-centric world?

    SciBite CSO and Founder Lee Harland shares his views on why ontologies are relevant in a machine learning-centric world and are essential to help "clean up" scientific data in the Life Sciences industry.

  2. High Performance Ontology Engineering

    One of the key aims of SciBite is to help our customers work with public ontologies in text mining applications. While these ontologies are very valuable resources, they are often built for the purpose of data organization, not text mining.


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