Genome-wide computational function prediction of Arabidopsis thaliana proteins by integration of multiple data sources
Although Arabidopsis thaliana is the best studied plant species, the biological role of one third of its proteins is still unknown. We developed a probabilistic protein function prediction method that integrates information from sequences, protein-protein interactions and gene expression. The method was applied to proteins from Arabidopsis thaliana. Evaluation of prediction performance showed that our method has improved performance compared to single source-based prediction approaches and two existing integration approaches. An innovative feature of our method is that enables transfer of functional information between proteins that are not directly associated with each other. We provide novel function predictions for 5,807 proteins. Recent experimental studies confirmed several of the predictions. We highlight these in detail for proteins predicted to be involved in flowering and floral organ development.
| Main Authors: | , , , |
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| Format: | Article/Letter to editor biblioteca |
| Language: | English |
| Subjects: | algorithm, biology, cell-death, family, flowering time, gene, generalized linear-models, networks, thaliana, transcription factor, |
| Online Access: | https://research.wur.nl/en/publications/genome-wide-computational-function-prediction-of-arabidopsis-thal |
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