Posts from December 2015

Animal models are crucial in the understanding of disease, the underlying pathways and the gene targets that play a role. One tool that has shown great value is the knockout mouse model.

The number of KO mouse models has increased massively since the first one in 1989, and mice models have been used successfully in increasing our understanding of diseases as varied as different cancers, diabetes, obesity, blindness, Huntington's disease, aggressive behaviour, and even drug addiction.

Understanding the landscape of KO mouse models for any particular disease area is important, and curated databases (e.g. IMPC or MGI) provide valuable data, but keeping track of new KO mouse models published in the scientific literature is challenging.

Peng Zhang, ‎Senior Staff Scientist at Regeneron Pharmaceuticals, uses Linguamatics I2E to tackle this challenge, and he presented on “Text Mining for Knockout Mice and Phenotypes” earlier this year.

 Diagram showing the set of KO genes involved in autoimmune phenotypes. All hits from both I2E and MGI were manually curated and only 479 unique KO genes were considered “true positive”. 61% true positives only came from I2E query and were not covered by MGI.


CMIOs-Importance-of-Clinical-NLP

 

The transition to new value-based payment models is spurring provider demand for technologies that enhance patient care and minimize safety risks, and in turn reduce costs. Of particular interest are tools to help providers predict the likelihood of potentially avoidable outcomes, such as a hospital readmission, pulmonary nodules turning cancerous or the contraction of sepsis.

According to a recent Linguamatics survey, most hospital CMIOs support the use of predictive models to improve the quality of care. In addition, CMIOs believe that these models can be enhanced with the use of Natural Language Processing (NLP) to access insightful data from unstructured chart notes.