Enhancing Cancer Classification from RNA Sequencing Data Using Deep Learning and Explainable AI

Abstract

Cancer is one of the most deadly diseases, costing millions of lives and billions of USD every year. There are different ways to identify the biomarkers that can be used to detect cancer types and subtypes. RNA sequencing is steadily taking the lead as the method of choice due to its ability to access global gene expression in biological samples and facilitate more flexible methods and robust analyses. Numerous studies have employed artificial intelligence (AI) and specifically machine learning techniques to detect cancer in its early stages. However, most of the models provided are very specific to particular cancer types and do not generalize. This paper proposes a deep learning and explainable AI (XAI) combined approach to classifying cancer subtypes and a deep learning-based approach for the classification of cancer types using BARRA:CuRDa, an RNA-seq database with 17 datasets for seven cancer types. One architecture is designed to classify cancer subtypes with around 100% accuracy, precision, recall, F1 score, and G-Mean. This architecture outperforms the previous methodologies for all individual datasets. The second architecture is designed to classify multiple cancer types; it classifies eight types within the neighborhood of 87% of validation accuracy, precision, recall, F1 score, and G-Mean. Within the same process, we employ XAI, which identifies 99 genes out of 58,735 input genes that could be potential biomarkers for different cancer types. We also perform Pathway Enrichment Analysis and Visual Analysis to establish the significance and robustness of our methodology. The proposed methodology can classify cancer types and subtypes with robust results and can be extended to other cancer types.