PMC:4331678 / 31260-32113 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":"ProMK and OMG also integrate the optimization of composite network and the classifier, but they only use the loss of the classifier on the individual networks to determine the weights. LIG first utilizes soft spectral clustering to partition each input individual network into several subnetworks, and then determines the weights of these subnetworks solely based on the loss of the classifier on them. SW constructs a composite network in advance, and then train a classifier on the composite network to predict protein functions. Since it optimizes the composite network and the classifier on the composite network into two separate objectives, it often loses to other comparing algorithms. These facts support our motivation to unifying the composite network construction based on kernel target alignment and the network-based predictor optimization.","tracks":[]}