title: 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 and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker's yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities.
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
authors.forename: Jana
authors.initials: J
authors.lastname: Kludas
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, Finland.
authors.forename: Mikko
authors.initials: M
authors.lastname: Arvas
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, Finland.
authors.forename: Sandra
authors.initials: S
authors.lastname: Castillo
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, Finland.
authors.forename: Tiina
authors.initials: T
authors.lastname: Pakula
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, Finland.
authors.forename: Merja
authors.initials: M
authors.lastname: Oja
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
authors.forename: Céline
authors.initials: C
authors.lastname: Brouard
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, Finland.
authors.forename: Jussi
authors.initials: J
authors.lastname: Jäntti
authors.affiliation: VTT Technical Research Centre of Finland, Espoo, Finland.
authors.forename: Merja
authors.initials: M
authors.lastname: Penttilä
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
authors.forename: Juho
authors.identifier: http://orcid.org/0000-0002-0705-4314
authors.initials: J
authors.lastname: Rousu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D030562:Databases, Protein; D019143:Evolution, Molecular; D005656:Fungal Proteins / Q000737:chemistry / Q000378:metabolism*; D016681:Genome, Fungal; D000069550:Machine Learning; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D055571:Secretory Pathway; D014242:Trichoderma / Q000378:metabolism*
chemical_list: D005656:Fungal Proteins