Predicting protein complexes using a supervised learning method combined with local structural information.

ABSTRACT: The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient

SUBMITTER: Dong Y 

PROVIDER: S-EPMC5858846 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature