IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research.

By Ying Chen, JD Elenee Argentinis, G. Weber. Clinical Therapeutics, 2016

404 citations recorded by the source corpus.

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.

Subfields: Information Systems and Management, Management Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/40af8cefbf2246431ebcd7153800ee619e026922

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Bin Li
2. Jonathan Guo

In Management Information Systems

1. Bin Li
2. Jonathan Guo

In Molecular Biology

1. Gregory Hager
2. Bhavna Josephine Antony
3. Pratik Shah
4. Bin Li
5. Jonathan Guo
6. Stefan Harrer
7. Jianying Hu

Citations in the corpus

• Artificial Intelligence Transforms the Future of Health Care. (2019, background): “20 Despite these novel approaches in drug discovery, there are important challenges, including data access and the fact that in general, different data sets are stored in a variety of repositories.”
• Surgical data science – from concepts toward clinical translation (2020, background): “The most widely discussed initiative so far is probably IBM® WatsonTM Health® (International Business Machines Corporation (IBM), Armonk, NY, USA), which initiated several projects such as Watson Medical Sieve, Watson For Oncology or Watson Clinical Matching that apply the Watson cognitive computing”
• Artificial Intelligence for Clinical Trial Design. (2019, background): “Insights could be used to educate future improved trial designs and also to investigate the potential relevance of already trialed drugs against comorbidities for drug repurposing [8].”
• The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries (2018, methodology): “recommendations made by the system were concordant with the ones made by the tumor board, but the system only took 40 sec to complete the process.(13,14)”