Table 3 Performance analysis of GSAQ and GSVQ approaches for salinity stress in rice.

Methods	100	200	300	400	500	1000	2000
t	<0.5	<0.01	<0.001	<0.001	<0.001	<0.0001	<0.0001
t	(>0.5)	(>0.01)	(>0.01)	(>0.01)	(>0.01)	(>0.1)	(>0.01)
F	<0.5	<0.01	<0.001	<0.001	<0.001	<0.0001	<0.0001
F	(>0.5)	(>0.01)	(>0.01)	(>0.01)	(>0.01)	(>0.01)	(>0.01)
MRMR	<0.01	<0.01	<0.01	<0.01	<0.0001	<0.0001	<0.0001
MRMR	(>0.1)	(>0.1)	(>0.01)	(>0.01)	(>0.01)	(>0.1)	(>0.01)
SU	<0.1	<0.1	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001
SU	(>0.1)	(>0.1)	(>0.1)	(>0.2)	(>0.01)	(>0.01)	(>0.5)
PCF	<0.01	<0.01	<0.01	<0.01	<0.01	<0.0001	<0.0001
PCF	(>0.01)	(>0.01)	(>0.01)	(>0.01)	(>0.01)	(>0.5)	(>0.5)
SRC	<0.01	<0.01	<0.01	<0.01	<0.01	<0.0001	<0.0001
SRC	(>0. 01)	(>0.1)	(>0.01)	(>0.01)	(>0.01)	(>0.001)	(>0.001)
SVM	<0.01	<0.01	<0.01	<0.01	<0.0001	<0.0001	<0.01
SVM	(>0.1)	(>0.01)	(>0.01)	(>0.01)	(>0.1)	(>0.1)	(>0.1)

FDR: False discovery rate; Gene sets: gene sets obtained from each method; (.): the values in parentheses indicate the FDR value computed through GSVQ approach; t: t-score; F: F-score; MRMR: Maximum Relevance Minimum Redundancy; SU: Symmetrical Uncertainty; PCF: Pearson’s Correlation Filter; SRC: Spearman’s Rank Correlation filter; SVM: Support Vector Machine with Recursive Feature Elimination.

Quick links

Search