@article{HansenMeyerFerrarietal.2017,
  author    = {Hansen, Bjoern Oest and Meyer, Etienne H. and Ferrari, Camilla and Vaid, Neha and Movahedi, Sara and Vandepoele, Klaas and Nikoloski, Zoran and Mutwil, Marek},
  title     = {Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana},
  series = {New phytologist : international journal of plant science},
  volume    = {217},
  journal   = {New phytologist : international journal of plant science},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0028-646X},
  doi       = {10.1111/nph.14921},
  pages     = {1521 -- 1534},
  year      = {2017},
  abstract  = {Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists.},
  language  = {en}
}