Selventa, focused on patient stratification and development of predictive biomarker panels, formed a scientific alliance with Linguamatics, a software solutions company. The firms will couple their analytical capabilities to efficiently extract complex life science knowledge in a computable, structured, biological expression language (BEL) format that can be used to interpret large-scale experimental data in the context of published literature.
Selventa’s discovery platform operates on top of a collection of scientific knowledge comprising a set of BEL statements. BEL is a structured language designed to represent scientific findings in a computable form with supporting contextual information (e.g., tissue, disease, species, publication, etc.). BEL is use-neutral, articulating an idea in a manner that is unambiguous and terse and conveys the facts and associated contexts without loss or ambiguity, according to Selventa.
Compared to a manual process of translating biological facts from the literature into BEL, Linguamatics’ I2E natural language processing (NLP) text-mining platform contains NLP-based capabilities that identify and extract relationships hidden in unstructured text. It offers increased speed, scale, and reproducibility, and the possibility to efficiently go back into a textual data source to pull out additional information that has become relevant, according to Linguamatics.
“This partnership is a great strategic fit to facilitate the representation of complex biological knowledge that can be recycled and maximized through our analytical platform,” remarks David de Graaf, Ph.D., president and CEO of Selventa. “Collaborating with Linguamatics will enable rapid yet comprehensive investigation of new areas of biology by extracting computable knowledge from unstructured text.”