Emerging concepts in immuno-oncology: Insights from natural language processing (NLP)-driven co-occurrence analysis

08 January 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Immuno-oncology, a rapidly evolving field at the forefront of cancer research, leverages the body’s immune system to fight cancer. In this follow up report, we extend our natural language processing (NLP)-based analysis to pinpoint the context of emergence of the previously identified emerging concepts. To achieve this relational analysis, we devised a method to identify terms co-occurring in a single sentence in the title or abstract of >350,000 journal and patent publications. The focus of the current study was on inter-group co-occurrences, namely between therapeutic targets, biomarkers, types of therapy, and types of cancer. Our analysis indicates that co-occurring concept pairs tend to be scattered between two extremes – potentially emerging concept pairs with rapid growth in publications over the last few years but an overall small number of publications and more well-established concept pairs with modest growth in publications but an overall larger volume of publications.

Keywords

immuno-oncology
co-occurrence analysis
natural language processing
cancer biomarker
cancer therapeutics

Supplementary materials

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Title
Supplementary information for Emerging concepts in immuno-oncology: Insights from natural language processing (NLP)-driven co-occurrence analysis
Description
Description of methods for co-occurrence analysis. Supplementary figures showing co-occurrences for intra-group emerging concepts in immuno-oncology.
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