PMC:4331677 / 22176-23616 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331677","sourcedb":"PMC","sourceid":"4331677","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331677","text":"Table 2 shows that the ACC and AUC scores of CS-PD are 56.5% and 79.9% respectively, which means the correct prediction rate is only slightly higher than random guess (the expect correct rate of random guess is 50%) and the comprehensive performance is not good. We guess that the poor performance of CS-PD is due to lacking of powerful classifier and it only serves as a feature extraction approach. BLM-NII preforms good in our data set, but not as well as in its origin data set (Yamanishi's \"Gold Standard\"). The AUC score of BLM-NII is 85.8% in our data set, while it is more than 98% in all four categories (enzyme, ion channel, GPCR, nuclear receptor) in its origin data set. The difference of data set could be the main cause of the AUC difference. It is a pity that not all the crystal structures of the targets in Yamanishi's data set are determined, and we could not perform our approach in the \"Gold Standard\". The ACC and AUC scores of RF are 0.743% and 0.851% respectively, which are similar with BLM-NII. The bagging ensemble procedure might promote the prediction ability of RF model. The ACC and AUC of FIM are 82.7% and 91.6% respectively, which is much higher than that of CS-PD, BLM-NII and RF. The ACC and AUC score is promoted more than 10% and 5% respectively, compared with state-of-the-art (BLM-NII). In short, the FIM have shown remarkable predictive ability and outperforms other three approaches in our data set.","tracks":[]}