This study investigates the associations between human microRNAs (miRNAs) and diseases, analyzing a manually curated database of miRNA-disease associations. The researchers constructed a human miRNA-associated disease network (MDN) and identified key patterns in miRNA-disease relationships. They found that miRNAs tend to show similar or different dysfunctional evidence for similar or different disease clusters. A negative correlation was observed between miRNA tissue specificity and the number of diseases they are associated with. Additionally, miRNA conservation was linked to disease associations, and miRNAs associated with the same disease often form predefined miRNA groups. These findings provide insights into miRNA-disease associations and suggest new methods for identifying novel disease-associated miRNAs. The study also revealed that miRNAs involved in the same disease cluster often exhibit similar dysfunction patterns. For example, miR-195 is upregulated in cardiovascular diseases and downregulated in cancers, indicating different underlying mechanisms for these diseases. The analysis further showed that miRNAs with high tissue specificity are more likely to be associated with specific diseases. For instance, miR-372 and miR-373 are specifically expressed in the placenta, while miR-206 is specifically expressed in skeletal muscle. These findings suggest that tissue-specific miRNAs may play a role in tissue-specific diseases. The study also found that miRNA conservation is associated with disease susceptibility. MiRNAs conserved across species are more likely to be associated with diseases. Additionally, miRNAs involved in the same disease often form miRNA families or clusters, suggesting that these miRNAs may have similar functions and roles in disease processes. The results were validated using a new dataset, confirming the robustness of the identified patterns. Overall, this study provides a comprehensive analysis of miRNA-disease associations, revealing important patterns that can aid in understanding the roles of miRNAs in diseases and in identifying novel disease-associated miRNAs.This study investigates the associations between human microRNAs (miRNAs) and diseases, analyzing a manually curated database of miRNA-disease associations. The researchers constructed a human miRNA-associated disease network (MDN) and identified key patterns in miRNA-disease relationships. They found that miRNAs tend to show similar or different dysfunctional evidence for similar or different disease clusters. A negative correlation was observed between miRNA tissue specificity and the number of diseases they are associated with. Additionally, miRNA conservation was linked to disease associations, and miRNAs associated with the same disease often form predefined miRNA groups. These findings provide insights into miRNA-disease associations and suggest new methods for identifying novel disease-associated miRNAs. The study also revealed that miRNAs involved in the same disease cluster often exhibit similar dysfunction patterns. For example, miR-195 is upregulated in cardiovascular diseases and downregulated in cancers, indicating different underlying mechanisms for these diseases. The analysis further showed that miRNAs with high tissue specificity are more likely to be associated with specific diseases. For instance, miR-372 and miR-373 are specifically expressed in the placenta, while miR-206 is specifically expressed in skeletal muscle. These findings suggest that tissue-specific miRNAs may play a role in tissue-specific diseases. The study also found that miRNA conservation is associated with disease susceptibility. MiRNAs conserved across species are more likely to be associated with diseases. Additionally, miRNAs involved in the same disease often form miRNA families or clusters, suggesting that these miRNAs may have similar functions and roles in disease processes. The results were validated using a new dataset, confirming the robustness of the identified patterns. Overall, this study provides a comprehensive analysis of miRNA-disease associations, revealing important patterns that can aid in understanding the roles of miRNAs in diseases and in identifying novel disease-associated miRNAs.