Table 8.

Related work.

Software	Author	Motivation	Advantage
plsRglm (R package)	Bertrand et al. (2010) [32]	(i) To deal with incomplete datasets using cross-validation (ii) To extend PLS regression to generalized linear models	(i) Provides formula support (ii) Several new classes and their generics (iii) Custom GLR models and graphics to assess the bootstrap based significance of the predictors
SVA-PLS	Chakraborty and Datta [30]	(i) To identify the genes that are differentially expressed between the samples from two different tissue types (ii) To identify the hidden effects of the underlying latent factors in a gene expression profiling study	(i) Relatively better at discovering a higher proportion of the truly significant genes (ii) Low error rate (iii) High sensitivity and specificity
SlimPLS	Gutkin et al. [33]	To obtain a low dimensional approximation of a matrix that is “as close as possible” to a given vector	(i) Focuses solely on feature selection (ii) Can be used as a pre-processing stage with different classifiers