DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants

BMC Bioinformatics

Table 3 Comparison of variant classification accuracy across different predictors in DMS and AlphaMissense datasets

	DMS						AlphaMissense
Predictors	Total	Hits	Errors	Accuracy (%)	Precision (%)	Recal (%)	Total	Hits	Errors	Accuracy (%)	Precision (%)	Recall (%)
DTreePred	557	540	17	96.95	98.17	87.70	548	530	18	96.72	84.47	97.75
MetaSVM	557	522	35	93.72	82.35	91.80	548	508	40	92.70	93.24	77.53
Envision	557	520	37	93.36	97.83	73.77	548	516	32	94.16	85.39	85.39
LRT	557	518	39	93.00	88.29	80.33	548	518	30	94.53	76.64	92.13
M-CAP	557	517	40	92.82	78.47	92.62	548	496	52	90.51	80.22	82.02
REVEL	557	517	40	92.82	84.17	82.79	548	509	39	92.88	71.55	93.26
CADD	557	514	43	92.28	100.00	64.75	548	523	25	95.44	68.99	100.00
SuSPect	557	492	65	88.33	80.65	61.48	548	514	34	93.80	81.63	88.89
MutationTaster	557	490	67	87.97	65.71	94.26	548	469	79	85.58	68.14	86.52
SNPs&GO	557	482	75	86.54	70.43	66.39	548	500	48	91.24	63.50	97.75
DeepSequence	177	150	27	84.75	94.12	66.67	177	158	19	89.27	52.98	100.00
Ndamage	557	456	101	81.87	54.88	96.72	548	429	119	78.28	42.79	100.00
Polyphen	557	444	113	79.71	52.09	91.80	548	425	123	77.55	41.90	98.88
Machine Learning Score No Homologs	557	408	149	73.25	27.12	13.11	548	423	125	77.19	18.97	12.36
Machine Learning Score	557	403	154	72.35	25.00	13.11	548	418	130	76.28	17.46	12.36
SIFT	557	394	163	70.74	42.49	95.08	548	371	177	67.70	33.46	100.00
PROVEAN	557	368	189	66.07	37.73	84.43	548	362	186	66.06	31.84	95.51
FATHMM	557	361	196	64.81	37.58	91.80	548	332	216	60.58	28.18	92.13

ISSN: 1471-2105