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IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research
Open accessAbstract
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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“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).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [54]
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Metadata
- Authors
- Chen, Y., Elenee Argentinis, J., & Weber, G.
- Affiliations
- IBM Research - Almaden (US)
- ORCID
- Chen, Ying
- 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
- hybrid
- doi.org
- Licence
- Locations
- Clinical Therapeutics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › 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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