Machine Learning of Protein Interactions in Fungal Secretory Pathways

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...

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Veröffentlicht in:	PloS one 2016-07, Vol.11 (7), p.e0159302-e0159302
Hauptverfasser:	Kludas, Jana, Arvas, Mikko, Castillo, Sandra, Pakula, Tiina, Oja, Merja, Brouard, Céline, Jäntti, Jussi, Penttilä, Merja, Rousu, Juho
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Amino Acid Sequence Amino acids Analysis Artificial intelligence Bacterial proteins Baking yeast Bioinformatics Biology and Life Sciences Biotechnology Classification Computer and Information Sciences Computer science Data transfer (computers) Databases, Protein Evolution, Molecular Fungal Proteins - chemistry Fungal Proteins - metabolism Fungi Gene expression Genetic distance Genome, Fungal Genomes Inference Information technology Input output International conferences Kernels Learning algorithms Life Sciences Machine Learning Medicine and Health Sciences Organisms Pezizomycotina Phylogenetics Physical Sciences Physiological aspects Predictions Protein interaction Protein Interaction Mapping Protein Interaction Maps Protein-protein interactions Proteins Research and Analysis Methods Robustness (mathematics) ROC Curve Saccharomyces cerevisiae Saccharomyces cerevisiae - genetics Saccharomyces cerevisiae - metabolism Secretion Secretory Pathway Species Taxonomy Trajectory planning Transfer learning Trichoderma - metabolism
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