The prediction accuracy was validated with a Jack-knife procedure. In statistical prediction, the following three crossvalidation methods are often used to examine a predictor for its effectiveness in practical applications: independent test dataset, sub-sampling test, and Jack-knife test [56] . However, as elucidated in references [26] and [27] , amongst the three cross-validation methods, the Jack-knife test is deemed the most objective that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] .