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Table 3 Comparison three different versions of our pipeline with Yang & Agarwal [40] and Lee [21] on 21 diseases

From: The assessment of efficient representation of drug features using deep learning for drug repositioning

MONDODisease nameYang & Agarwa1Lee (Random forest)Lee (N-Net)Ours {s}Ours {g, s}Ours {s, p}
0000190ventricular fibrillation0.740.850.780.810.820.79
0001627dementia0.620.890.790.830.890.81
0002049thrombocytopenia0.500.670.720.950.950.94
0002243hemorrhagic disease0.590.690.670.971.000.96
0003620peripheral nervous system disease0.910.640.690.920.930.91
0004975alzheimer disease0.680.620.610.860.890.84
0004976amyotrophic lateral sclerosis0.580.730.590.960.980.95
0004979asthma0.530.730.680.730.850.69
0004981atrial fibrillation0.500.800.790.870.920.85
0004985bipolar disorder0.690.840.820.870.900.86
0005015diabetes mellitus0.660.790.710.920.890.91
0005027epilepsy0.620.750.700.810.870.79
0005041glaucoma0.600.850.580.900.930.89
0005059leukemia0.690.790.550.970.970.97
0005062lymphoma0.720.850.550.970.940.97
0005068myocardial infarction0.640.700.680.920.910.91
0005180parkinson disease0.700.740.690.810.860.78
0005275lung disease0.700.780.680.940.900.93
0005578arthritis0.670.730.520.910.920.90
0008114obsessive-compulsive disorder0.950.790.760.970.950.97
0011122obesity0.640.720.660.670.440.71