Literature-Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Abstract

Biomedical knowledge is growing at an astounding pace with a majority of this knowledge represented as scientific publications. Text mining tools and methods represent automatic approaches for extracting hidden patterns and trends from this semi-structured and unstructured data. In biomedical text mining, literature-based discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches have proven to successfully reduce the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting them with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 remains a core task in text mining. Currently, the ABC principle-based two approaches namely open discovery and closed discovery are mostly explored in the LBD process. This review starts with a general introduction about text mining followed by biomedical text mining followed by a brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in the LBD process. This review discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks-based LBD models and their future aspects. Additionally, the potential of Large Language Models in enriching the LBD process is discussed with challenges and solutions and finally reviews the key biomedical discoveries generated through LBD approaches in biomedicine and concludes with the current limitations and future directions of LBD.