IBM announced advancements in its Watson cognitive computing capability, moving beyond analyzing data for known answers in record speeds to providing supporting evidence to back up those answers and prompt new questions.
The new capability helps researchers accelerate scientific breakthroughs by simplifying the complexities and revealing the connections in massive amounts of data, said Rob High, vice president and CTO of the IBM Watson Group, in an interview with eWEEK.
“We're entering an extraordinary age of data-driven discovery," said Mike Rhodin, senior vice president of IBM Watson Group, in a statement. "The proliferation of data is exasperating an organization's ability to discover connections between disparate data. This announcement is a natural extension of Watson's cognitive computing intelligence, empowering research, developers and industry experts, with powerful insights and connections in data, giving scientists the ability to make connections with data that others don't see, which can lead to significant breakthrough discoveries."
High said IBM Watson Discovery Advisor is a system that can visually reveal patterns and pinpoint connections in data to accelerate the discovery process. The cloud-based Watson Discovery Advisor is designed to scale and accelerate discovery by research teams, reducing the time needed to test hypotheses and formulate conclusions that can advance their work, from months to days and days to just hours, by bringing new levels of speed and precision to research and development, IBM said.
The upgraded Watson Discovery Advisor not only maps previously unknown correlations between key data points and understands nuances in natural language, but also understands the language of chemical compounds and how they interact, addressing critical dimensions in discovery in life sciences and other industries, IBM said. The system can be taught to learn and understand biology, intellectual property (IP), and law, among other industries, having profound implications for their R&D efforts.
Researchers and scientists from leading life sciences organizations, including academic, pharmaceutical and research centers, have begun deploying IBM's new Watson Discovery Advisor to overcome the complex challenges of absorbing, analyzing and creating hypotheses from the millions of scientific papers available in public databases. According to the National Institutes for Health, a typical researcher reads about 23 scientific papers per month, which translates to nearly 300 per year. A new scientific research paper is published on average every 30 seconds, making it humanly impossible to keep up with the ever-growing body of scientific material available, IBM said.
The implications are astounding to advancing R&D across a variety of industries. For example, in 2013, the top 1,000 research and development companies spent $638 billion annually on research alone, according to Booz & Company. Meanwhile, it takes 10 to 15 years on average for a pharmaceutical treatment to go from initial research stage into practice, according to the Pharmaceutical Research and Manufacturers of America.
Leading life sciences organizations are deploying Watson Discovery Advisor to advance discoveries in on-going research projects, including Baylor College of Medicine, Johnson & Johnson and The New York Genome Center.
High said Baylor and IBM scientists demonstrated a possible new path for generating scientific questions that may be helpful in the longterm in developing new treatments for disease. In a matter of weeks, biologists and data scientists using the Baylor Knowledge Integration Toolkit (KnIT), based on Watson technology, accurately identified proteins that modify p53, an important protein related to many cancers, which can eventually lead to better efficacy of drugs and other treatments.
This is a feat that would have taken researchers years to accomplish without Watson's cognitive capabilities, High said. Watson analyzed 70,000 scientific articles on p53 to predict proteins that turn on or off p53's activity. This automated analysis led the Baylor cancer researchers to identify six potential proteins to target for new research. These results are notable, considering that over the last 30 years, scientists averaged one similar target protein discovery a year, IBM said.