BIM-Ken: Identifying Disease-Related miRNA Biomarkers Based on Knowledge-Enhanced Bio-Network
Abstract
1. Introduction
2. Materials and Methods
2.1. Data
2.2. BIM-Ken Method
2.2.1. MiRNA Cooperation Network Generation
2.2.2. MiRNA Cooperation Network Enhancement
- (1)
- The reconstruction loss
- (2)
- Functional consistency constraint
- (3)
- Difference prompt constraint
2.2.3. Key miRNA Module Identification
3. Experimental Settings
4. Results and Discussion
4.1. Performance Comparison
4.2. Ablation Study
4.3. Module Biomarker Detected by BIM-Ken for the Renal Cell Carcinoma
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Datasets | Disease Types | Features | Samples | Classes |
---|---|---|---|---|
GSE34496 | Head and neck squamous cell carcinoma | 812 | 69 | 2 |
GSE36802 | Prostate cancer | 812 | 42 | 2 |
GSE41922 | Breast cancer | 264 | 54 | 2 |
GSE67139 | Hepatocellular carcinoma | 812 | 115 | 2 |
GSE76260 | Prostate cancer | 787 | 64 | 2 |
GSE78775 | Gastric cancer | 818 | 56 | 2 |
GSE116251 | Renal cell carcinoma | 769 | 36 | 2 |
GSE142699 | Acute myeloid leukemia | 769 | 48 | 2 |
GSE158284 | Glioblastoma | 214 | 41 | 2 |
Datasets | BIM-Ken | SVM-RFE | INDEED | GRACES | t-Test | GCNCC | NetRank | DDRM |
---|---|---|---|---|---|---|---|---|
GSE34496 | 94.50 ± 1.90 | 87.36 ± 3.70 * | 91.62 ± 2.70 * | 91.38 ± 2.02 * | 93.64 ± 2.61 | 85.43 ± 4.46 * | 91.86 ± 1.84 * | 89.98 ± 1.86 * |
GSE36802 | 93.10 ± 3.89 | 82.50 ± 5.90 * | 87.40 ± 1.82 * | 91.95 ± 3.83 | 87.55 ± 3.60 * | 84.85 ± 4.96 * | 91.95 ± 2.66 | 89.50 ± 3.79 |
GSE41922 | 90.57 ± 1.46 | 85.70 ± 2.28 * | 84.73 ± 3.31 * | 91.43 ± 2.89 | 86.07 ± 3.15 * | 87.00 ± 4.11 * | 86.77 ± 2.01 * | 85.83 ± 3.56 * |
GSE67139 | 87.17 ± 1.84 | 77.35 ± 1.73 * | 82.92 ± 1.83 * | 81.48 ± 3.09 * | 84.14 ± 1.72 * | 83.52 ± 2.67 * | 85.18 ± 2.94 | 81.06 ± 2.04 * |
GSE76260 | 79.38 ± 2.92 | 72.57 ± 5.67 * | 74.88 ± 3.76 * | 71.67 ± 2.59 * | 73.10 ± 3.09 * | 71.17 ± 3.62 * | 74.74 ± 4.24 * | 72.07 ± 3.24 * |
GSE78775 | 80.23 ± 2.64 | 74.37 ± 5.57 * | 74.33 ± 4.85 * | 68.80 ± 6.08 * | 77.50 ± 3.21 | 58.97 ± 3.62 * | 79.87 ± 3.42 | 69.10 ± 4.59 * |
GSE116251 | 85.33 ± 4.40 | 78.33 ± 3.97 * | 80.42 ± 2.95 * | 73.58 ± 4.55 * | 83.25 ± 4.15 | 64.00 ± 11.55 * | 82.83 ± 4.07 | 79.25 ± 3.63 * |
GSE142699 | 97.40 ± 0.91 | 91.80 ± 2.54 * | 91.75 ± 3.08 * | 94.80 ± 1.44 * | 93.95 ± 3.18 * | 94.60 ± 3.55 * | 94.50 ± 1.97 * | 95.55 ± 3.02 |
GSE158284 | 92.20 ± 3.12 | 83.85 ± 1.86 * | 86.05 ± 2.58 * | 85.95 ± 3.82 * | 84.70 ± 3.05 * | 85.30 ± 4.46 * | 82.85 ± 3.35 * | 87.95 ± 2.90 * |
Ave | 88.88 | 81.54 | 83.79 | 83.45 | 84.88 | 79.43 | 85.62 | 83.37 |
W/T/L | 9/0/0 | 9/0/0 | 8/0/1 | 9/0/0 | 9/0/0 | 9/0/0 | 9/0/0 |
Datasets | BIM-Ken | SVM-RFE | INDEED | GRACES | t-Test | GCNCC | NetRank | DDRM |
---|---|---|---|---|---|---|---|---|
GSE34496 | 94.60 ± 1.79 | 89.05 ± 4.56 * | 91.45 ± 2.31 * | 92.05 ± 2.93 * | 93.60 ± 1.87 | 92.40 ± 3.92 | 91.40 ± 2.65 * | 91.55 ± 2.95 * |
GSE36802 | 89.17 ± 5.57 | 76.00 ± 9.00 * | 86.17 ± 3.93 | 91.33 ± 5.32 | 84.83 ± 3.55 | 89.17 ± 4.25 | 90.33 ± 3.91 | 88.17 ± 5.47 |
GSE41922 | 90.67 ± 2.22 | 85.25 ± 3.38 * | 86.92 ± 5.11 | 92.17 ± 3.50 | 85.08 ± 4.88 * | 92.75 ± 4.36 | 87.08 ± 3.65 * | 86.75 ± 2.87 * |
GSE67139 | 84.93 ± 2.14 | 73.67 ± 1.85 * | 80.57 ± 2.72 * | 78.67 ± 3.09 * | 81.00 ± 2.01 * | 86.03 ± 2.83 | 85.20 ± 3.92 | 80.00 ± 3.23 * |
GSE76260 | 76.00 ± 4.81 | 70.25 ± 6.57 * | 74.42 ± 7.73 | 73.33 ± 6.21 | 72.83 ± 6.14 | 73.50 ± 4.76 | 72.83 ± 7.83 | 70.42 ± 6.10 * |
GSE78775 | 79.17 ± 3.54 | 75.00 ± 6.48 | 75.67 ± 4.73 | 67.00 ± 9.78 * | 77.00 ± 4.43 | 62.00 ± 4.89 * | 82.67 ± 6.68 | 67.33 ± 5.89 * |
GSE116251 | 79.50 ± 7.62 | 73.50 ± 7.84 | 76.50 ± 10.01 | 73.50 ± 5.80 | 78.00 ± 8.56 | 69.00 ± 9.94 * | 79.00 ± 6.99 | 79.50 ± 5.99 |
GSE142699 | 94.50 ± 2.09 | 90.00 ± 2.36 * | 92.67 ± 4.39 | 92.33 ± 2.96 | 93.00 ± 4.29 | 93.00 ± 4.29 | 91.00 ± 3.06 * | 92.33 ± 5.73 |
GSE158284 | 97.17 ± 3.34 | 87.00 ± 2.05 * | 90.17 ± 3.55 * | 89.00 ± 5.73 * | 88.83 ± 4.01 * | 90.33 ± 6.18 * | 85.33 ± 2.46 * | 89.83 ± 2.77 * |
Ave | 87.30 | 79.97 | 83.84 | 83.26 | 83.80 | 83.13 | 84.98 | 82.88 |
W/T/L | 9/0/0 | 9/0/0 | 7/0/2 | 9/0/0 | 6/1/2 | 6/0/3 | 8/1/0 |
Datasets | BIM-Ken | SVM-RFE | INDEED | GRACES | t-Test | GCNCC | NetRank | DDRM |
---|---|---|---|---|---|---|---|---|
GSE34496 | 94.17 ± 2.86 | 84.50 ± 6.67 * | 92.33 ± 5.10 | 89.67 ± 4.07 * | 93.67 ± 3.83 | 73.83 ± 6.76 * | 92.67 ± 2.96 | 86.67 ± 3.69 * |
GSE36802 | 96.83 ± 4.68 | 88.50 ± 8.29 * | 89.00 ± 5.34 * | 92.50 ± 3.54 * | 90.33 ± 4.96 * | 81.00 ± 6.54 * | 93.67 ± 4.83 | 90.83 ± 4.39 * |
GSE41922 | 90.83 ± 3.36 | 86.17 ± 4.01 * | 82.17 ± 4.72 * | 90.50 ± 5.78 | 87.50 ± 4.32 | 79.67 ± 6.47 * | 87.00 ± 3.31 * | 84.00 ± 6.05 * |
GSE67139 | 89.50 ± 2.17 | 81.03 ± 4.57 * | 85.40 ± 2.11 * | 84.67 ± 4.22 * | 87.37 ± 2.49 | 81.43 ± 3.45 * | 85.30 ± 3.63 * | 81.93 ± 2.32 * |
GSE76260 | 82.58 ± 3.59 | 74.83 ± 6.26 * | 75.08 ± 5.59 * | 69.75 ± 2.83 * | 73.42 ± 3.25 * | 69.17 ± 3.26 * | 76.08 ± 6.16 * | 73.92 ± 4.16 * |
GSE78775 | 81.50 ± 2.99 | 74.00 ± 6.81 * | 73.17 ± 7.68 * | 70.50 ± 6.19 * | 77.83 ± 5.21 | 56.33 ± 7.06 * | 76.50 ± 5.90 * | 70.33 ± 6.23 * |
GSE116251 | 90.50 ± 4.97 | 84.50 ± 5.50 * | 84.00 ± 8.76 | 74.00 ± 5.16 * | 88.00 ± 4.83 | 62.00 ± 13.58 * | 86.50 ± 5.30 | 78.00 ± 4.83 * |
GSE142699 | 100.00 ± 0.00 | 93.33 ± 4.51 * | 90.33 ± 5.02 * | 97.50 ± 2.26 * | 94.67 ± 3.58 * | 97.33 ± 3.06 * | 97.83 ± 2.84 * | 99.33 ± 1.41 |
GSE158284 | 79.50 ± 5.99 | 76.00 ± 4.59 | 76.50 ± 6.26 | 77.50 ± 5.89 | 76.00 ± 6.58 | 77.00 ± 4.22 | 77.00 ± 10.06 | 85.00 ± 7.45 |
Ave | 89.49 | 82.54 | 83.11 | 82.95 | 85.42 | 75.31 | 85.84 | 83.33 |
W/T/L | 9/0/0 | 9/0/0 | 9/0/0 | 9/0/0 | 9/0/0 | 9/0/0 | 8/0/1 |
Module id | Classification Accuracy Rate | Sensitivity | Specificity |
---|---|---|---|
M_1 | 97.10 ± 0.30 | 97.51 ± 0.39 | 95.71 ± 0.00 |
M_2 | 94.82 ± 0.32 | 96.35 ± 0.17 | 89.57 ± 1.79 |
M_3 | 97.33 ± 0.22 | 97.51 ± 0.34 | 96.71 ± 0.69 |
M_4 | 92.31 ± 0.24 | 95.15 ± 0.28 | 82.57 ± 0.60 |
M_5 | 97.11 ± 0.34 | 97.72 ± 0.22 | 95.00 ± 1.01 |
M_6 | 96.11 ± 0.35 | 96.63 ± 0.41 | 94.29 ± 1.17 |
M_7 | 87.49 ± 0.44 | 94.39 ± 0.35 | 63.71 ± 1.20 |
Module id | Pathway | p-Value |
---|---|---|
M_1 | hsa04010:MAPK signaling pathway | 2.45 × 10−3 |
M_2 | hsa05211:Renal cell carcinoma | 7.71 × 10−4 |
M_3 | hsa05211:Renal cell carcinoma | 2.15 × 10−3 |
M_4 | hsa04350:TGF-beta signaling pathway | 1.20 × 10−3 |
M_5 | hsa05211:Renal cell carcinoma | 4.36 × 10−3 |
M_6 | hsa05211:Renal cell carcinoma | 7.51 × 10−6 |
M_7 | hsa04350:TGF-beta signaling pathway | 8.72 × 10−3 |
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Zhang, Y.; Dong, K.; Sun, W.; Gao, Z.; Zhang, J.; Lin, X. BIM-Ken: Identifying Disease-Related miRNA Biomarkers Based on Knowledge-Enhanced Bio-Network. Genes 2025, 16, 902. https://doi.org/10.3390/genes16080902
Zhang Y, Dong K, Sun W, Gao Z, Zhang J, Lin X. BIM-Ken: Identifying Disease-Related miRNA Biomarkers Based on Knowledge-Enhanced Bio-Network. Genes. 2025; 16(8):902. https://doi.org/10.3390/genes16080902
Chicago/Turabian StyleZhang, Yanhui, Kunjie Dong, Wenli Sun, Zhenbo Gao, Jianjun Zhang, and Xiaohui Lin. 2025. "BIM-Ken: Identifying Disease-Related miRNA Biomarkers Based on Knowledge-Enhanced Bio-Network" Genes 16, no. 8: 902. https://doi.org/10.3390/genes16080902
APA StyleZhang, Y., Dong, K., Sun, W., Gao, Z., Zhang, J., & Lin, X. (2025). BIM-Ken: Identifying Disease-Related miRNA Biomarkers Based on Knowledge-Enhanced Bio-Network. Genes, 16(8), 902. https://doi.org/10.3390/genes16080902