SciBite's semantic entity recognition and extraction engine. Ultra-fast and designed to understand the complexity of scientific text, TERMite identifies key terms in any document in seconds, transforming raw text into machine-readable data.
To achieve the best results in exploring target-disease biology, researchers regularly apply multiple approaches. Reviewing direct protein-disease relationships is a simple but powerful technique for gaining an initial view but will only get you so far. Read on to find out how SciBite can help you to identify and explore phenotypic associations within a disease.
High Performance Ontology Engineering11th July 2016
One of the key aims of SciBite is to help our customers work with public ontologies in text mining applications. While these ontologies are very valuable resources, they are often built for the purpose of data organisation, not text mining. The reliance on vanilla public ontologies in text-mining will often lead to very poor results.
Over the last 18 months, there has been a predictable surge in research on the Zika virus as the scientific community try to better understand the disease area. We decided to take a look at this topic to see how much research we being done across the globe and what phenotypes/symptoms have been mentioned to date.
Announcing the latest version of our flagship text analytics software for life sciences, TERMite. TERMite is a powerful platform for the analysis of complex biomedical text in real time to aid discovery and decision making in areas such as drug repurposing, disease phenotype analysis, adverse event reporting, competitor intelligence, business analysis and many more.