This study aims to analyze the patterns of microRNA (miRNA)-disease associations by comprehensively examining manually collected human miRNA-disease data. The authors constructed a human miRNA-associated disease network (MDN) using a bipartite graph model, where nodes represent miRNAs and diseases, and edges connect diseases that share at least one common miRNA. Key findings include: 1. **Dysfunctional Evidence Patterns**: Diseases within the same cluster (e.g., cancers or cardiovascular diseases) tend to show similar dysfunctions (e.g., up-regulation or down-regulation) of shared miRNAs, while diseases in different clusters often exhibit different dysfunctions. 2. **Tissue-Specificity and Disease Association**: A negative correlation was found between the tissue-specificity index of a miRNA and the number of diseases it is associated with. Tissue-specific miRNAs are more likely to be involved in diseases specific to their tissue of expression. 3. **MiRNA Conservation and Disease**: MiRNAs that are evolutionarily conserved are associated with diseases more frequently than those that are human-specific. Additionally, miRNAs associated with diseases show lower SNP occurrence probabilities compared to those not associated with diseases. 4. **MiRNA Sets and Diseases**: MiRNAs in families and clusters are more likely to be associated with the same disease. For example, miRNAs in the miR-8 family are often involved in thyroid cancer, and neighboring miRNAs in the miR-17 cluster are associated with hematopoietic malignancies. 5. **Validation**: The patterns observed in the original dataset were validated using a new dataset from November 2007 to June 2008, confirming the robustness of the findings. These findings provide insights into the complex relationships between miRNAs and diseases, suggesting potential avenues for identifying novel disease biomarkers and understanding the mechanisms underlying miRNA-related diseases.This study aims to analyze the patterns of microRNA (miRNA)-disease associations by comprehensively examining manually collected human miRNA-disease data. The authors constructed a human miRNA-associated disease network (MDN) using a bipartite graph model, where nodes represent miRNAs and diseases, and edges connect diseases that share at least one common miRNA. Key findings include: 1. **Dysfunctional Evidence Patterns**: Diseases within the same cluster (e.g., cancers or cardiovascular diseases) tend to show similar dysfunctions (e.g., up-regulation or down-regulation) of shared miRNAs, while diseases in different clusters often exhibit different dysfunctions. 2. **Tissue-Specificity and Disease Association**: A negative correlation was found between the tissue-specificity index of a miRNA and the number of diseases it is associated with. Tissue-specific miRNAs are more likely to be involved in diseases specific to their tissue of expression. 3. **MiRNA Conservation and Disease**: MiRNAs that are evolutionarily conserved are associated with diseases more frequently than those that are human-specific. Additionally, miRNAs associated with diseases show lower SNP occurrence probabilities compared to those not associated with diseases. 4. **MiRNA Sets and Diseases**: MiRNAs in families and clusters are more likely to be associated with the same disease. For example, miRNAs in the miR-8 family are often involved in thyroid cancer, and neighboring miRNAs in the miR-17 cluster are associated with hematopoietic malignancies. 5. **Validation**: The patterns observed in the original dataset were validated using a new dataset from November 2007 to June 2008, confirming the robustness of the findings. These findings provide insights into the complex relationships between miRNAs and diseases, suggesting potential avenues for identifying novel disease biomarkers and understanding the mechanisms underlying miRNA-related diseases.