NLP Framework to Summarize p53–FBW7 Pathway Alterations and Associated miRNAs in CRC

Abstract

The increasing intricacies of molecular pathways involved in CRC, especially focusing on the p53-FBW7 pathway network and the microRNAs (miRNAs) that regulate them, present significant difficulties when synthesizing knowledge from the exploding body of biomedical literature. Conventional methods of manual literature review have proven inadequate for capturing and analyzing such multidimensional interactions in a timely fashion. In this paper, we describe an innovative NLP-based approach for mining CRC-related pathway changes and miRNA interactions. The architectural framework uses domain-driven data extraction methods along with sophisticated pre-processing, named entity recognition using transformers, and relation extraction techniques to extract important biological entities and their relationships. The relations extracted by the framework are then encoded within a knowledge graph capturing pathway-level dynamics of gene-protein-miRNA relationships, which can provide an integrated perspective on gene-protein-miRNA relationships. Furthermore, the transformer-based summarizer also provides summarized insights related to p53-FBW7 axis. The model aims to bring together computational intelligence and molecular oncology through a comprehensive, automated, and interpretable process for knowledge integration in biomedical sciences. Expected benefits will involve greater efficiency in research, the ability to identify key regulatory mechanisms, and contributions to precision medicine and drug discovery initiatives. Despite being conceptual, the paper introduces a novel method that combines NLP with cancer pathways, holding promise for extensive development in the future.