The target discovery application built with metaphactory on top of the customer's knowledge graph empowers data scientists, immunologists or systems biologists, to explore data and gain meaningful and actionable insights for their daily tasks.
Pharma companies have a lot of internal data about compounds they have developed and tested, clinical studies they have run, or research projects they are running. Whether these compounds, clinical studies or research projects have been successful or not, often the resulting data is difficult to access or reuse for new endeavours. On top of that, there is a considerable amount of public data available on compounds, drugs, diseases and their genetic associations, proteins and their coding genes, etc. Consequently, researchers struggle to analyze all this information and extract comprehensive insights from it.
metaphacts has joined forces with Ontotext to support this Swiss multinational healthcare company in building a knowledge graph based solution that provides highly interlinked information across various data sources and offers a modular approach to R&D data discovery and knowledge consumption.
One of the key use cases the customer focused on as part of their knowledge graph adoption and implementation journey was improving their current drug discovery process. The smart knowledge discovery solution they were building had the goal of helping researchers leverage all available proprietary and public data to:
- better use and repurpose existing drugs;
- find relevant targets for new drugs;
- perform targeted searches to find biomolecular information for specific indications or groups of indications.
The main challenge was how to extract knowledge from data residing in multiple sources, in heterogeneous formats, and across various business units. In the existing drug development process, researchers looking to leverage preclinical or clinical data for particular compounds first had to find the relevant data sources. Then they had to search in each system independently, sometimes requiring further assistance from IT departments to perform their queries.
After collecting all required pieces of information, they had to integrate all different parts into a consistent report/record. This process was very time consuming and prone to errors, and even after all that effort, a lot of information still remained locked in unstructured data.
On top of that, as each of these reports/records related to one-off research, the resulting data, just like the data from previous drug testing (whether successful or not) was not reusable for other projects. Even when a new research had similar parameters to a previous project, researchers couldn’t build on existing results and had to start from scratch.
The solution: A preclinical knowledge discovery platform
The preclinical knowledge discovery platform jointly developed by metaphacts and Ontotext enabled the customer to transform and accelerate their drug development process. The solution covered the following steps:
- Modeling the domain with metaphactory, based on the Pharma company's specific information needs;
- Integrating all proprietary data with GraphDB and mapping the model to diverse data sources to build a customized knowledge graph;
- Providing access to relevant datasets from Ontotext's inventory of more than 200 preloaded public datasets and ontologies in RDF format, covering various knowledge domains (such as genomics, proteomics, metabolomics, molecular interactions and biological processes, pharmacology, clinical, medical and scientific publications);
- Building an intuitive user experience with metaphactory which allows end users to search and filter results, discover relevant information, bookmark and share results with their colleagues, add and edit data, build customized dashboards, etc.
The resulting application is rich and intuitive. It is also easy to adjust, extend and reuse to meet new business needs and cater to new use cases or end user groups. Thanks to its ability to represent data as a network of relationships, the created knowledge graph does not only provide access to diverse data sources, but also reveals previously unknown relationships in the data.
The solution employed a standardized data model, ontologies and vocabularies. Metadata was used to encode the meaning of the data and unique identifiers ensured that all meta-levels in the data were searchable, accessible, shareable and traceable. The resulting data makes it a lot easier to find, reproduce and reuse research results. It also includes clear provenance for addressing any data consistency issues coming from the highly dynamic environment of drug development.
- 5-6 weeks to go from idea to a production-ready solution;
- Adaptive data model to accommodate changing business needs;
- Leveraging proprietary knowledge with global data and keeping it up-to-date;
- Empowering end users to work with tons of diverse data and get meaningful insights;
- Driving digital transformation for better business outcomes.
Why choose metaphacts and Ontotext
With the new preclinical knowledge discovery platform developed by metaphacts and Ontotext, domain users at the customer site have fast and easy access to information via a live knowledge graph where the data for all integrated public datasets is constantly updated.
Combining a highly scalable and robust RDF database like GraphDB with a big inventory of ready-to-use biomedical datasets as well as Ontotext's proven methodology for semantic data integration enabled the customer to quickly create a large-scale customized knowledge graph.
On top of that, metaphactory's intuitive search and data exploration capabilities enables researchers working for this customer to interact with huge volumes of data consumed from the knowledge graph and use and reuse the knowledge locked in this data in a meaningful way.