Chen Y, Argentinis E, and Weber G. IBM Watson: How Cognitive Computing Can Be Applied to Big
Data Challenges in Life Sciences Research. Clinical Therapeutics 2016; 38(4): 688-701.

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Life sciences researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking novel insights and accelerating breakthroughs. Ironically, although more data are available than ever, only a fraction is being integrated, understood, and analyzed. The challenge lies in harnessing volumes of data, integrating the data from hundreds of sources, and understanding their various formats.

New technologies such as cognitive computing offer promise for addressing this challenge because cognitive solutions are specifically designed to integrate and analyze big datasets. Cognitive solutions can understand different types of data such as lab values in a structured database or the text of a scientific publication. Cognitive solutions are trained to understand technical, industry-specific content and use advanced reasoning, predictive modeling, and machine learning techniques to advance research faster.

Watson, a cognitive computing technology, has been configured to support life sciences research. This version of Watson includes medical literature, patents, genomics, and chemical and pharmacological data that researchers would typically use in their work. Watson has also been developed with specific comprehension of scientific terminology so it can make novel connections in millions of pages of text. Watson has been applied to a few pilot studies in the areas of drug target identification and drug repurposing. The pilot results suggest that Watson can accelerate identification of novel drug candidates and novel drug targets by harnessing the potential of big data.

The full list of the top 100 articles on artificial intelligence and artificial intelligence in medicine are published here:

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Anthony Chang, MD, MBA, MPH, MS