There's been a lot of excitement around recent studies on immuno-oncology in which the body’s immune defences are corralled to fight cancer. Experts consider it the most exciting advance since the development of chemotherapy half a century ago.

Many of our customers are involved in anti-cancer approaches based on modulation of immunosuppressive properties of immune cells; and are using I2E to help generate insight around immuno-oncology and the tumor microenvironment (TME). Cancers can be viewed as complex ‘rogue’ organs, with malignant cells surrounded by blood vessels and a variety of other cells, including immune cells, fibroblasts, lymphocytes, and more. The tumor cells and the surrounding non-transformed cells interact constantly, and developing a better understanding of these TME interactions is a valuable approach in immuno-oncology drug development.

Knowledge in this field is growing very rapidly which makes it very difficult for scientists to capture it manually, both because of the volume of publications, but also the variety and complexity of information.

Challenges include ensuring a thorough search to capture relationships between genes/proteins and their effect or correlation on or with a variety of cellular actors. These cellular actors included many of the immune system cells currently under investigation for immunotherapeutic approaches to oncology.

I2E provides the capability to find and extract these interactions from textual data, including capture of negation where needed. I2E allows efficient and effective searches over millions of text documents, and can harmonize the output to enable computational post-processing and visualization of these complex data.


The Linguamatics Booth #345 at this year’s Bio-IT Conference (April 5-7 in Boston) offers the ideal opportunity to catch up with the latest developments in text mining.

Here are 3 reasons to meet the market leader in text analytics for life science and healthcare:


Guy Singh, Linguamatics Senior Manager, Product and Strategic Alliances, explains the key differences between keyword search and text mining.

See the full 52 second video below.

 

To learn more about how text mining works, check out our other video resources:

 


Tom Schmidt, Managing Editor, IDG Strategic Marketing Services interviewed Dr. Jane Reed, Head of Life Science Strategy, Linguamatics, on how pharma and biotech companies use text analytics to reduce the time and cost of their clinical trials and get drugs to market faster.

The common statistic is that over 80% of data lies in unstructured text. Often, the way that people write things, whether in patents, healthcare records, or scientific literature, it's not easy to pull out the nuggets that are going to help with those decisions, whether around the real world value of your product, regulatory compliance, or many other different areas. Text analytics has to play a part in addressing many problems because of the volume of data that is unstructured.

Watch the full interview below.


With the ongoing focus on healthcare outcomes-based payment models, pharmaceutical companies face powerful pressures to demonstrate not just safety and efficacy of a new treatment, but also both cost effectiveness and comparative effectiveness. This means they must show that their agent is not only better than placebo but also better than other agents. Comparative effectiveness of any particular treatment can be established by interventional clinical trials, observational real-world evidence studies, or systematic review and meta-analysis. Access to on-going and past clinical trials via trial registries provides much valuable information, but effective search can be hindered by issues such as search vocabularies and problems of searching the unstructured text.

Merck recently published a paper, demonstrating the success of a text-mining pipeline that overcomes these issues and extracts key information for comparative effectiveness research from clinical trial registries. Researchers in the Informatics IT group wanted to search clinical trial registries (NIH ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), and Citeline Trialtrove) and synthesize comparative effectiveness data for a set of Merck drugs, in order to: