Predicting protein-protein interactions through sequence-based deep learning.

ABSTRACT:

Motivation

High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data.

Results

We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on sever

SUBMITTER: Hashemifar S 

PROVIDER: S-EPMC6129267 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature