Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features

Table 5

The comparison of the predictive results between this paper and existing methods.

Method		HSP families
		HSP20	HSP40	HSP60	HSP70	HSP90	HSP100
iHSP-PseRAAAC^a	Sn (%)	87.68	95.31	66.87	79.15	51.72	69.41
	Sp (%)	96.36	84.87	98.93	86.54	99.89	99.84
	MCC	0.82	0.99	0.69	0.54	0.3	0.83
	Acc (%)	—	—	—	—	—	—
PredHSP^b	Sn (%)	92.16	96.09	79.75	91.17	72.41	82.35
	Sp (%)	97.16	86.26	97.24	91.97	99.12	98.08
	MCC	0.87	0.83	0.72	0.71	0.7	0.71
	Acc (%)	96.36	91.91	95.96	91.87	98.43	97.48
ir-HSP^c	Sn (%)	94.63	97.45	67.92	88.49	75	88.89
	Sp (%)	96.61	95.13	98.86	98.84	99.76	99.57
	MCC	0.8718	0.9276	0.7307	0.8871	0.8112	0.8846
	Acc (%)	96.28	96.47	96.61	97.52	99.17	99.17
Our predictive model	Sn (%)	100	98.33	100	100	100	100
	Sp (%)	99.92	100	99.92	99.82	100	100
	MCC	1	0.99	1	0.99	1	1
	Acc (%)	99.93	99.72	99.93	99.85	100	100

^aFeng et al. [21]. ^bKumar et al. [25]. ^cMeher et al. [26].