  In this paper, a new method for PPI (proteinprotein interaction) prediction
is proposed. In PPI prediction, a reliable and sufficient number of training
samples is not available, but a large number of unlabeled samples is in hand.
In the proposed method, the denoising auto encoders are employed for learning
robust features. The obtained robust features are used in order to train a
classifier with a better performance. The experimental results demonstrate the
capabilities of the proposed method.
  Protein-protein interaction; Denoising auto encoder;Robust features;
Unlabelled data;

Machine Learning;Computational Engineering, Finance, and Science