Cited by 466Computational Drug Discovery MethodsBiomedical Text Mining and OntologiesArtificial Intelligence in Healthcare and Education

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

Open access
Chen, Ying, Elenee Argentinis, JD, Weber, Griff·2016·Clinical Therapeutics

Abstract

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 acc…

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Crossref · 10.1016/j.clinthera.2015.12.001

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  • AI models developed by IBM Watson have been used to predict individual responses to different diabetes medications, helping clinicians tailor treatments to each patient's unique genetic and metabolic profile.

    “For instance, AI models developed by IBM Watson have been used to predict individual responses to different diabetes medications, helping clinicians tailor treatments to each patient’s unique genetic and metabolic profile (54).”

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Authors
Chen, Y., Elenee Argentinis, J., & Weber, G.
Affiliations
IBM Research - Almaden (US)
Venue
Clinical Therapeutics
Publisher
Elsevier BV
Published
2016-04
Volume
38
Issue
4
Pages
688-701
ISSN
0149-2918 (ISSN-L)
OpenAlex
W2342299174
Cited by
466
References
28
Citations by year
2026: 19 · 2025: 36 · 2024: 42 · 2023: 38 · 2022: 37 · 2021: 68 · 2020: 58 · 2019: 66 · 2018: 63 · 2017: 31 · 2016: 8
FWCI
4.34
Type
journal article
Language
en
Open access
Locations
  • Clinical Therapeutics (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
IBM Watson, cognitive computing, big data, biology research, drug target identification, drug repurposing, scientific literature analysis, data integration, genomic data, patents, machine learning
MeSH terms
Biological Science Disciplines, Cognition, Humans, Research, Databases, Factual, Drug Delivery Systems, Informatics, Drug Repositioning

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