HuntMi: an efficient and taxon-specific approach in pre-miRNA identification

BMC Bioinformatics

Table 3 Detailed classification results

Classifier	Parameter selection (III)			Parameter + threshold selection (IV)
	SE	SP	G _m	SE	SP	G _m
human
N. Bayes	87.98	96.33	92.06	91.97	93.93	92.94
Perceptron	69.56	99.84	83.34	94.17	94.99	94.58
SVM	69.56	99.85	83.34	92.53	95.69	94.10
R. forest	68.21	99.85	82.53	91.53	96.34	93.90
APLSC	94.88	92.14	93.50
SMOTE + SVM	77.67	99.02	87.69
arabidopsis
N. Bayes	86.99	98.91	92.76	91.30	97.77	94.48
Perceptron	80.09	99.95	89.47	93.04	97.47	95.23
SVM	80.07	99.96	89.47	93.04	98.95	95.95
R. forest	83.55	99.94	91.38	95.22	99.04	97.11
APLSC	96.09	90.42	93.21
SMOTE + SVM	88.71	99.64	94.02
animal
N. Bayes	85.54	95.53	90.40	88.83	92.81	90.79
Perceptron	74.03	99.65	85.89	91.78	95.13	93.44
SVM	72.04	99.74	84.77	90.67	90.09	93.34
R. forest	72.52	99.72	85.04	92.00	95.21	93.59
APLSC	91.93	91.13	91.53
SMOTE + SVM	84.56	98.68	91.35
plant
N. Bayes	83.56	97.56	90.29	87.48	95.84	91.57
Perceptron	77.30	99.80	87.83	89.64	97.38	93.43
SVM	73.07	99.85	85.42	89.46	97.93	93.60
R. forest	78.41	99.81	88.47	90.65	97.96	94.24
APLSC	92.77	89.39	91.07
SMOTE + SVM	81.31	99.32	89.86
virus
N. Bayes	93.21	93.21	93.21	95.74	92.37	94.04
Perceptron	87.77	98.10	92.79	94.08	95.71	94.89
SVM	90.31	98.10	94.12	95.38	95.35	95.37
R. forest	88.59	98.45	93.39	93.26	96.31	94.77
APLSC	96.61	92.97	94.77
SMOTE + SVM	91.99	97.14	94.53
microPred
N. Bayes	80.32	94.27	87.02	89.43	87.91	88.67
Perceptron	82.35	99.37	90.46	90.74	94.65	92.67
SVM	79.31	99.72	88.93	89.29	97.01	93.07
R. forest	75.83	99.66	86.94	91.89	96.36	94.10
APLSC	91.45	90.96	91.21
SMOTE + SVM	87.70	98.83	93.10

ISSN: 1471-2105