A two-stage learning method for protein-protein interaction prediction
This addresses a domain-specific challenge in bioinformatics for researchers, but it is incremental as it builds on existing methods like autoencoders.
The paper tackles the problem of protein-protein interaction prediction where labeled training data is scarce but unlabeled data is abundant, by using denoising autoencoders to learn robust features and then training a classifier, resulting in improved performance as demonstrated experimentally.
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;