PMC:1892782 / 6552-7152 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1892782","sourcedb":"PMC","sourceid":"1892782","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1892782","text":"The proportion of true positive and false positive rate (ROC curve) for each combination of descriptors is summarized in Fig. 1. It reveals which combination of descriptors achieves optimal classification of the alignments. The ROC curves can be evaluated by the area under the curve (AUC), which states the similarity of the ROC curve to a step function. The steeper the true positive rate increases while staying at its maximum value for different values of false positive rates, the better the input alignments can be separated. The best AUC of 99% is achieved when all four descriptors are taken.","tracks":[]}