A biomarker identification model from protein protein interaction network using natural language processing and graph convolutional network

Abstract

A biomarker identification model, integrating natural language processing (NLP) and graph convolutional neural network (GCN), offers a novel approach to enhance a simple neural network’s ability to capture the contextual semantics of genes and extract spatial feature information by utilizing gene ontology (GO) annotations. First, we explore gene expression datasets to identify differentially expressed genes (DEGs) and construct a protein-protein interaction (PPI) network. By employing Word2Vec, an NLP algorithm, for vectorizing GO annotations, our model reveals complex biological relationships among genes. GO annotations are crucial as they provide comprehensive information about gene functions, biological processes, and cellular components, thus augmenting our understanding of how genes interact within the network. Integrating multi-layered GCN facilitates effective learning of complex semantic relations and spatial feature information within the PPI network. Experiments on publicly available datasets of Glioblastoma Multiforme (GBM), the most aggressive form of brain tumour, demonstrate that our model significantly enhances biomarker identification compared to existing state-of-the-art methods, showcasing its potential for advancing GBM research and clinical decision-making.