Machine Learning of Protein Interactions in Fungal Secretory Pathways.

ABSTRACT: In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR) in supervised

SUBMITTER: Kludas J 

PROVIDER: S-EPMC4956264 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature