Multi-label ℓ₂-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks.

ABSTRACT: Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in most cases as most PPI networks are seldom semantically annotated. In this work, we extend ℓ₂-regularized logistic regression to the scenario of multi-label learning for predicting the activation/inhibition relationships in human PPI networks. The phenomenon that both activation and inhibition relationships exist between two interacting proteins is computationally modelled by multi-label l

SUBMITTER: Mei S 

PROVIDER: S-EPMC5098220 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature