Selventa, a personalized healthcare company focused on stratification of patients and development of predictive biomarker panels based on disease-driving mechanisms, announced that it has formed a strategic scientific alliance with Linguamatics, a software solutions company that provides knowledge extraction through its I2E natural language processing (NLP) text mining platform. The alliance will bring together established analytical capabilities of both companies 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.
“The Selventa and Linguamatics collaboration shows how precise, detailed information can be automatically extracted from the literature and provided in a format suitable for further analysis and reasoning,” said David Milward, Ph.D., Chief Technology Officer at Linguamatics. “This will allow re-use of knowledge from the literature, at greater scale and speed.”
Selventa’s discovery platform operates on top of a collection of scientific knowledge (knowledgebase) comprised of 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, terse, and conveys the facts and associated contexts without loss or ambiguity. BEL, along with the BEL Framework, is available through a portal to the scientific community to promote the collection, sharing and interchange of structured scientific knowledge (www. belframework.org).
Compared to a manual process of translating biological facts from the literature into BEL, Linguamatics’ I2E platform contains powerful NLP-based capabilities that efficiently identify and extract relationships hidden in unstructured text, to generate structured scientific knowledge that can subsequently be used for comprehensive biological investigation and analysis. It offers dramatically 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.
“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,” said 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. This will lead to innovation on many fronts, such as Next Generation Sequencing, where well-structured information for reasoning has been limited. As a result, this will have the potential to provide a deeper, content-rich, scientific investigation to our partners, and ultimately help their future discovery efforts. We see a great potential for positive impact on future drug development decisions in areas such as translational medicine and clinical proof-of-concept stages.”
Date: October 31, 2011
Source: Selventa, Linguamatics
