Affordable Access

Access to the full text

Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme

Authors
  • Chen, Kuan-Hsi
  • Wang, Tsai-Feng
  • Hu, Yuh-Jyh
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Jun 10, 2019
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12859-019-2907-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundAlthough various machine learning-based predictors have been developed for estimating protein–protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein–protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features.ResultsWe developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors.ConclusionWe introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.

Report this publication

Statistics

Seen <100 times