Table 4.
The hub genes list by machine learning method.
| Reference | Data source | Method | Genes |
|---|---|---|---|
| Liu Z. et al. (2021) | GSE63061, GSE85426 | LASSO, SVM-RFE | ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL |
| Yu et al. (2021) | GSE33000, GSE36980, GSE48350, GSE5281, GSE122063, GSE106241, GSE4226, GSE97760, GSE63060, GSE63061 | LASSO regression | BDNF, WWTR1 |
| Abyadeh et al. (2022) | GSE118553, GSE44768, GSE48350, GSE5281, GSE33000, GSE44770, GSE36980, GSE122063, GSE132903, GSE29378 | RRA | ELK-1, GATA1, GATA2 |
| Duan et al. (2022) | GSE1297 GSE28146 GSE36980 | Logistic regression | ATP2A2, ATP6V1D, CAP2, SYNJ1 |
| Zhu et al. (2023a) | GSE118553, GSE122063, GSE36980, GSE48350, GSE5281, GSE36980, GSE48350, GSE5281, GSE118553, GSE122063, GSE36980, GSE132903, GSE5281, GSE140829, ADNI dataset | RRA, LASSO regression | CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, TBC1D2, APOE |
| Zhu et al. (2023b) | GSE118553, GSE122063, GSE36980, GSE33000, GSE48350, GSE44770, GSE5281 | RRA | TAC1 |
| Guo et al. (2023) | GSE173955, GSE203206, GSE15222, GSE97760 | random forest (RF) binary classifier, Gaussian mixture model (GMM), linear model (LM), and support vector machine (SVM) | LCK, ZAP70, CD44, CD2, SNAP25, CD3E, CXCL8, HIST1H3J, IL12RB2, STAT4 |
| Liu C. et al. (2023) | GSE5281, GSE48350 | logistic regression and RF | KDELR1, SPTAN1, CDC16, RBBP6 |
| Sekaran et al. (2023) | GSE36980 | Logistic Regression (LR), RF, Linear Support Vector Machines (L-SVM), Naive Bayes (NB), and Multilayered Perceptron Neural Network (MLP-NN) | ORAI2, TPI1, STIM1, TRPC3 |
| Jin et al. (2023) | GSE63060 | DGS-TabNet | AVIL, NDUFS4 |
| Duan et al. (2022) | GSE1297 GSE28146 GSE36980 | Logistic regression | ATP2A2, ATP6V1D, CAP2, SYNJ1 |