Table 2 The results of all machine learning algorithms used for screening important genes related to AML prognosis
From: Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
Items | Machine learning algorithms | Intersection genes | |||||||
|---|---|---|---|---|---|---|---|---|---|
LASSO | RF | SVM | XGBOOST | IdaProfile | nbFuncs | caretFuncs | lrFuncs | ||
Important genes | TFF3 | TFF3 | TFF3 | TFF3 | TFF3 | TFF3 | TFF3 | HK3 | DNM1 |
CTSE | HOXA7 | SUSD3 | HOXA7 | SUSD3 | SUSD3 | HOXA7 | CTSE | SUSD3 | |
SLC25A21 | MEIS1 | S100P | MEIS1 | S100P | S100P | DNM1 | PF4 | MEIS1 | |
BMX | SUSD3 | CTSE | CTSE | CYP4F2 | CYP4F2 | MEIS1 | SPINK2 | - | |
SUSD3 | DNM1 | CYP4F2 | PF4 | CTSE | CTSE | SPINK2 | DNM1 | - | |
CYP4F2 | - | BMX | DNM1 | BMX | BMX | CTSE | NMU | - | |
HOXA7 | - | SLC25A21 | VNN2 | SLC25A21 | SLC25A21 | SLC25A21 | S100P | - | |
DNM1 | - | DNM1 | SUSD3 | DNM1 | DNM1 | BMX | FGF13 | - | |
FGF13 | - | HOXA7 | RFESD | C17orf99 | C17orf99 | CYP4F2 | EPB42 | - | |
C17orf99 | - | C17orf99 | HOXA5 | HOXA7 | HOXA7 | HOXA5 | CYP4F2 | - | |
CA3 | - | RFESD | SLC25A21 | LIN7A | LIN7A | SUSD3 | CLEC5A | - | |
SPINK2 | - | SPINK2 | CYP4F2 | RFESD | FGF13 | S100P | SUSD3 | - | |
S100P | - | LIN7A | RTN1 | FGF13 | RFESD | - | MEIS1 | - | |
MEIS1 | - | FGF13 | SPINK2 | HK3 | HK3 | - | CA3 | - | |
NKX2.3 | - | HK3 | BMX | SPINK2 | SPINK2 | - | BMX | - | |
RTN1 | - | CA3 | HK3 | CA3 | CA3 | - | - | - | |
CLEC5A | - | MEIS1 | NKX2.3 | CLEC5A | CLEC5A | - | - | - | |
PF4 | - | HOXA5 | CA3 | MEIS1 | MEIS1 | - | - | - | |
HOXA5 | - | CLEC5A | S100P | NMU | NMU | - | - | - | |
EPB42 | - | NMU | - | HOXA5 | HOXA5 | - | - | - | |
- | - | VNN2 | - | VNN2 | VNN2 | - | - | - | |
- | - | PF4 | - | PF4 | PF4 | - | - | - | |
- | - | - | - | RTN1 | RTN1 | - | - | - | |
- | - | - | - | NKX2.3 | NKX2.3 | - | - | - | |
- | - | - | - | EPB42 | - | - | - | - | |
- | - | - | - | IL1R2 | - | - | - | - | |