Network-based drug sensitivity prediction

Ahmed, Khandakar Tanvir; Park, Sunho; Jiang, Qibing; Yeu, Yunku; Hwang, TaeHyun; Zhang, Wei

doi:10.1186/s12920-020-00829-3

BMC Medical Genomics

Table 2 Prediction results of the top-20 drugs

From: Network-based drug sensitivity prediction

Drugs	E net CC	E net Net	PLSR CC	PLSR Net	RF CC	RF Net	SVR CC	SVR Net	DNN CC	DNN Net	GNN	Embed
SW157765	0.7606	0.7823	0.7212	0.7093	0.7956	0.8239	0.7228	0.7286	0.6615	0.3557	0.3406	0.3580
SW157692	0.5879	0.6098	0.5479	0.5402	0.6297	0.6984	0.5621	0.5738	0.5421	0.3354	0.3566	0.3364
SW134727	0.4957	0.4393	0.5456	0.5400	0.5226	0.4875	0.5248	0.5138	0.4722	0.3373	0.3654	0.3934
SW005017	0.4885	0.4793	0.5257	0.5097	0.4874	0.4690	0.5562	0.5510	0.5025	0.3769	0.3514	0.3393
SW072554	0.4784	0.4873	0.5000	0.4976	0.4870	0.4921	0.4505	0.4560	0.3644	0.3225	0.3821	0.3548
SW198886	0.4997	0.4444	0.4867	0.4702	0.4325	0.4074	0.4781	0.4673	0.4776	0.3072	0.4064	0.3553
SW197409	0.4163	0.4231	0.4775	0.4705	0.4401	0.4321	0.4709	0.4747	0.4392	0.3416	0.3853	0.3672
SW134963	0.4369	0.4034	0.4838	0.4727	0.4744	0.4193	0.4706	0.4616	0.4144	0.3462	0.3652	0.3691
SW006981	0.4223	0.4229	0.4558	0.4465	0.4162	0.3955	0.4541	0.4523	0.4367	0.3377	0.3439	0.3471
SW096640	0.3883	0.4111	0.4348	0.4292	0.4803	0.4774	0.4229	0.4196	0.3596	0.3413	0.3820	0.3806
SW148608	0.3968	0.4017	0.4167	0.4033	0.4110	0.4058	0.4702	0.4571	0.3950	0.3615	0.3637	0.3570
SW023297	0.3222	0.3711	0.3915	0.3846	0.4151	0.4313	0.4732	0.4824	0.4624	0.3605	0.3658	0.3341
SW074797	0.4224	0.4696	0.3923	0.4058	0.4223	0.4426	0.3768	0.3915	0.3614	0.3122	0.3955	0.3470
SW015134	0.4077	0.4283	0.3945	0.3924	0.4340	0.4654	0.3872	0.3909	0.3706	0.3374	0.3812	0.3459
SW043997	0.4006	0.3585	0.4345	0.4298	0.4160	0.4145	0.4062	0.4019	0.3655	0.3115	0.4248	0.3431
SW208072	0.3912	0.3910	0.4213	0.4236	0.4335	0.4489	0.4087	0.4109	0.3307	0.3247	0.3576	0.3394
SW113135	0.3942	0.4196	0.3974	0.4093	0.4603	0.4727	0.3643	0.3756	0.3152	0.3304	0.3820	0.3273
SW088073	0.2786	0.3024	0.4111	0.4020	0.4148	0.4146	0.4607	0.4607	0.4257	0.3127	0.3686	0.3536
SW018825	0.3759	0.3817	0.4028	0.4109	0.4093	0.4130	0.4118	0.4278	0.3383	0.3302	0.3661	0.3304
SW041995	0.3861	0.4071	0.3994	0.4125	0.4263	0.4313	0.3661	0.3681	0.3477	0.3405	0.3276	0.3554

The best results across all the methods are italic. Embed represents the network-based embedding method (Fig. 1) and GNN represents the graphical neural network model (Fig. 2)

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