Sequence-based prediction of protein protein interaction using a deep-learning algorithm

By Tanlin Sun, Bo Zhou, Luhua Lai, Jianfeng Pei. 2017

341 citations recorded by the source corpus.

Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

Subfields: Molecular Biology

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In Molecular Biology

  1. Chun-chao Lo
  2. Xiuwen Liu
  3. Wei Wu
  4. Jinfeng Zhang
  5. Yang Chen
  6. Chenran Wang
  7. Yuan Zhang

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