PMC:4331678 / 30310-31259 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331678","sourcedb":"PMC","sourceid":"4331678","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331678","text":"Another observation is that SW often loses to other comparing methods on MacroF1 and MicroF1. There are two reasons for this behavior: (i) SW applies binary classification on the composite network, but the other comparing algorithms do network-based classification for all the labels; (ii) MicroF1 and MacroF1 are computed based on the transformed binary indicative label vectors, and the binary indicative vector is derived from the largest elements of fi for each protein (see the metric definition in the Additional File 1 for more information); the other three metrics do not apply the binary transformation of fi. MSkNN uses a classifier ensemble to integrate multiple networks, and sometimes gets comparable results to other algorithms, which take advantage of the composite network to fuse multiple networks. These results show that classifier ensembles are another effective way to fuse multiple data sources for protein function prediction.","tracks":[]}