Majority Vote Cascading

Abstract

A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of high confidence to predict the labels of other nodes in subsequent rounds. We call this a majority vote cascade. Several cascade variants are tested in a stringent cross-validation experiment on PPI networks from S. cerevisiae and D. melanogaster, and we show that for many different settings with several alternative confidence functions, cascading improves the accuracy of the predictions. A list of the most confident new label predictions in the two networks is also reported. Code, networks for the cross-validation experiments, and supplementary figures and tables appear at http://bcb.cs.tufts.edu/cascade.

MUNDO: protein function prediction embedded in a multispecies world

“…In the most common form of k -fold cross-validation, k − 1 of the folds are used for training and the remaining fold is used for testing . Because we were especially interested in performance of function prediction methods when the amount of training data is sparse, we copied the experimental setup of Lazarsfeld et al . (2021) , which ‘inverts’ the relative sizes of the training and test data, so that, only one of the folds is used for training, and the other k − 1 have all their annotations removed and are placed in the testing set.…”