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Emerging Concepts in Immuno-Oncology: Insights from Natural Language Processing-Driven Co-Occurrence Analysis.
Kavita A Iyer1, Rumiana Tenchov1, Julian M Ivanov1
1CAS, A Division of the American Chemical Society, Columbus, Ohio 43210, United States.
This study uses natural language processing to analyze over 350,000 publications, identifying emerging immuno-oncology concepts. It highlights co-occurring terms in cancer research to guide future scientific discovery.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
- Computational Biology
Background:
- Immuno-oncology harnesses the immune system to combat cancer.
- Previous analysis identified emerging concepts in immuno-oncology research.
- A follow-up study is needed to contextualize these emerging concepts.
Purpose of the Study:
- To pinpoint the emergence context of previously identified immuno-oncology concepts.
- To perform relational analysis of co-occurring terms in scientific literature.
- To identify rapidly growing but under-published concept pairs in cancer research.
Main Methods:
- Utilized natural language processing (NLP) for literature analysis.
- Developed a method to identify terms co-occurring within single sentences of titles/abstracts.
- Analyzed over 350,000 journal and patent publications.
Main Results:
- Identified intergroup co-occurrences between therapeutic targets, biomarkers, therapy types, and cancer types.
- Highlighted potentially emerging co-occurring concept pairs with rapid publication growth.
- Found concept pairs with high publication growth but a small overall publication count.
Conclusions:
- The relational analysis provides valuable contextual information for immuno-oncology.
- Findings can help direct future research efforts in promising directions.
- Understanding co-occurring concepts aids in navigating the evolving landscape of cancer research.
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