Repository logo

Text mining for contexts and relationships in cancer genomics literature

Published version

Repository DOI

Change log


Brown, Jason 
Zheng, Huiyuan 
Chan, Adelyne 


Motivation: Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies. Results: We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts. Availability and implementation: Code and data are available here:


Funder: UK Research and Innovation; DOI:

Funder: Amazon Machine Learning Research Award

Funder: Cancer Research UK Cambridge Institute; DOI:

Funder: Biotechnology and Biological Sciences Research Council; DOI:


Journal Title


Conference Name

Journal ISSN


Volume Title



Oxford University Press
British Council (65BX18MNIB)