PMC:4331676 / 19135-19838 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331676","sourcedb":"PMC","sourceid":"4331676","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331676","text":"To evaluate the PSSM-DT method, we analyzed the impact of parameter LG on the predictive performance of the proposed model. The predictive results of SVM-PSSM-DT with different values of LG on the benchmark dataset by using five-fold cross validation is shown in Figure 1. As can be seen from the Figure, the value of LG has modest impact on both the ACC and MCC metrics. The ACC firstly increases to a maximum value and then slightly goes down as the LG value increases. So we can conclude that SVM-PSSM-DT achieves the best performance when LG = 5, which mean that the dimension of the feature space applied in this work is 2000. Therefore, the parameter LG was set as 5 for the following experiments.","tracks":[]}