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.


Earlier this year, Linguamatics announced our new Connected Data Technology for federated search, and in our newest version, I2E 4.4, we build on this to take another step along the path of better data interoperability. I2E 4.4 introduces a more powerful way to customize your text analytics results using enhanced linkouts in the HTML output, enabling you, for example, to connect your text-mined data to structured content.

Linkouts enable you to link out to, or pull in, additional information relating to the preferred terms (PTs) or concept identifiers (NodeIDs) in your query results. They can be hyperlinks, images or customized output. For example, you can configure linkouts to see information from an external website by clicking on the concept in the text-mined query results. Alternatively, it is possible to enable the interface to display an image in the query results, such as a chemical structure, instead of the preferred term.

This new functionality means you can use linkouts to enhance query results, by enabling you to access additional related information to provide more context or metadata for your search. So, for example, a search for chemicals from ChEBI could link directly from the preferred term in your results to the webpage for that concept on the EBI web site (e.g. Cyclosporine), whilst a gene name in the same result links to EntrezGene (e.g. ICAM1).


At the October Text Mining Summit, we had speakers from pharma, biotech and academia presenting on an amazing range of different applications of text analytics to provide value within the drug discovery-development pipeline. Over a day and a half we heard from a dozen external speakers from healthcare and pharma, all sharing their enthusiasm for the value that text analytics can bring to the drug discovery, development and delivery environments.

Work presented by UNCC researchers using I2E to understand potential health effects of plant phytochemical: Network map of text-mined associations linking Plant to phytochemical; Phytochemical to human genes; Human genes to biological pathways; Pathways linked to human health phenotypes.

The life science applications ranged from safety, target discovery and alerting, genotype-phenotype annotations, clinical trial analytics, phytochemicals as potential nutraceuticals, and patent landscaping for antibody-drug conjugates.

Back by popular demand, Wendy Cornell (ex-Merck) presented on gaining value from internal preclinical safety reports using I2E, which we’ve discussed in blog posts here before.


There seems to be a certain buzz around rare and orphan diseases. Following the Findacure meeting I attended last month, there are two recent events I’d like to mention.

Firstly, I attended the first Cambridge Rare Disease Network summit, held in Cambridge UK, with a fantastic line-up of speakers from a range of professions to discuss current and new initiatives in rare disease. The debates ranged from the use of next generation sequencing for diagnostics, to crowd-sourcing both for science and funding, to drug repurposing, to the views of payers and the issues around pricing.

For me it was also a reminder, particularly from some of the parent speakers, of the impact that rare disease has on individuals and families. All too often we are so busy with the day-to-day of research and business that it's easy to lose sight of the ideal end-goal - treatments for all adults, all children, affected by these disparate and often devastating diseases.

Secondly, this month the FDA released new draft guidance “to navigate the difficult road to approval of drugs for rare diseases”.


I attended the Findacure “Drug Repurposing for Rare Diseases” event last week; a small symposium with an interesting mix of attendees – academics, pharma, patient groups, vendors.  The main focus was networking, inspired by a series of short talks (see Findacure blog for more information).

  • 6,000 to 8,000 identified rare diseases (prevalence less than 5 in 10,000)
  • Only approximately 200 have licenced treatments – large unmet need
  • 1 in 17 people (6-8% of population) will develop a rare disease
  • 30-40 million people in US, 30-40 million in Europe
  • 75% of all rare diseases affect children

With the changing landscape from “blockbuster” to more personalised “nichebuster” therapeutics, and the incentives provided by regulatory bodies (such as FDA’s Orphan Drug Designation), rare diseases are an increasing focus of many of Linguamatics’ pharma and biotech customers.

So, I hear you ask – how does text analytics fit into rare diseases drug discovery?  It’s simple: Information associated with rare diseases is essential at many stages of drug discovery and development.  And, this essential information is often buried in unstructured text - in different data sources, with differing formats, vocabs, etc.