Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach

This paper presents a novel approach to accurately classify the hallmarks of cancer, which is a crucial task in cancer research. Our proposed method utilizes the Bidirectional Encoder Representations from Transformers (BERT) architecture, which has shown exceptional performance in various downstream applications. By applying transfer learning, we fine-tuned the pre-trained BERT model on a small corpus of biomedical text documents related to cancer. The outcomes of our experimental investigations demonstrate that our approach attains a noteworthy accuracy of 94.45%, surpassing almost all prior findings with a substantial increase of at least 8.04% as reported in the literature. These findings highlight the effectiveness of our proposed model in accurately classifying and comprehending text documents for cancer research, thus contributing significantly to the field. As cancer remains one of the top ten leading causes of death globally, our approach holds great promise in advancing cancer research and improving patient outcomes. Keywords: BERT, cancer hallmark classification, transfer learning, deep learning, natural language processing 1. Introduction Cancer is one of the most difficult sicknesses for individuals in many parts of the world today, including epigenetic and genetic mutations (Jiang et al., 2020). Up to now, millions of people have died due to this disease in the world (Organization, 2008). The study of cancer has a long history that stretches from the past to the present and has consistently drawn the attention of biomedical researchers.