Disease Similarity and Disease Clustering

In bioinformatics research, identifying the common characteristics of human diseases is crucial for understanding disease-disease associations. In this direction, disease similarity and disease clustering methodologies provide many insights by identifying underlying patterns, disease mechanisms, and other factors associated with disease causation and progression. Furthermore, the information and outcomes from these insights can inform drug repurposing, precision medicine, and other medical and healthcare applications. However, there are limitations in computational techniques, data integration, and disease knowledge that prevent or hinder a comprehensive understanding of the relationships between diseases. With our study, we aim to assist ongoing research by providing an integrated overview of disease similarity and disease clustering. We will examine the biomedical databases, disease databases and vocabularies, and disease-related information commonly used for this research. Then, we analyze some of the computational methods and integrated approaches used to quantify disease-disease associations. Additionally, we provide insights that can be derived from disease similarity and clustering analysis.