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Biomedical knowledge is growing in an astounding pace with a majority of thisknowledge is represented as scientific publications. Text mining tools andmethods represents automatic approaches for extracting hidden patterns andtrends from this semi structured and unstructured data. In Biomedical Textmining, Literature Based Discovery (LBD) is the process of automaticallydiscovering novel associations between medical terms otherwise mentioned indisjoint literature sets. LBD approaches proven to be successfully reducing thediscovery time of potential associations that are hidden in the vast amount ofscientific literature. The process focuses on creating concept profiles formedical terms such as a disease or symptom and connecting it with a drug andtreatment based on the statistical significance of the shared profiles. Thisknowledge discovery approach introduced in 1989 still remains as a core task intext mining. Currently the ABC principle based two approaches namely opendiscovery and closed discovery are mostly explored in LBD process. This reviewstarts with general introduction about text mining followed by biomedical textmining and introduces various literature resources such as MEDLINE, UMLS, MESH,and SemMedDB. This is followed by brief introduction of the core ABC principleand its associated two approaches open discovery and closed discovery in LBDprocess. This review also discusses the deep learning applications in LBD byreviewing the role of transformer models and neural networks based LBD modelsand its future aspects. Finally, reviews the key biomedical discoveriesgenerated through LBD approaches in biomedicine and conclude with the currentlimitations and future directions of LBD.

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