Life science companies face a two-pronged challenge: making their legacy data Findable, Accessible, Interoperable, and Reusable (FAIR), and ensuring newly generated data are also FAIR. Curating data with domain-specific ontologies help to overcome these challenges because ontologies structure data in a way that ticks all the FAIR boxes.
It is important to ensure the legacy and newly generated data adhere to the FAIR principles. If legacy data is not FAIR or made FAIR, it leads to productivity loss, slows down time to market, and return on investment. Similarly, ensuring that data is FAIR is critical in preventing them from becoming part of the legacy data companies are already contending with. One solution to this could be smart data entry, where scientists use ontology-powered type ahead when inputting assay information, such that data are normalized with ontologies at the point of entry.
Ontologies provide unique identifiers with associated names and synonyms. This helps with the normalization of scientific language. Tagging data with these identifiers makes it easier for scientists to search and analyze, as it includes results containing synonyms or associated terms that the ontology recognizes as related to the search query. Furthermore, since ontologies are based on an accepted community model, data are presented in a widely understood way, reducing the number of instances competing terminology is used.
Crucially, ontologies ensure data are machine-readable, harmonizing them for analysis with Artificial Intelligence (AI) and machine learning. With data structured in an ontology, companies can be sure their algorithms are learning from the full picture of information, reducing the risk of error, and improving the accuracy of results.
If organizations want to use AI to drive precision therapy breakthroughs, they must start getting their data in order by implementing FAIR principles. FAIR unlocks the long-term potential of data, enabling faster and more detailed analysis. For the business, there are significant productivity gains. For the patient, new paths are identified in the quest to create new-targeted therapies and better outcomes. Ontologies will be at the center of this transformation, de-siloing, standardizing, and harmonizing data sources to transform unstable text and images into data that powers discovery.
Click here to read the original article published by Technology Networks.
Please give your feedback on this article or share a similar story for publishing by clicking here.
