Highly Efficient Framework for Predicting Interactions Between Proteins

Zhu Hong You, Meng Chu Zhou, Xin Luo, Shuai Li

Research output: Contribution to journalArticlepeer-review

98 Scopus citations


Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in humans. Hence, automatic methods for human PPI-detection are highly desired. This work proposes a novel framework, i.e., Low-rank approximation-kernel Extreme Learning Machine (LELM), for detecting human PPI from a protein's primary sequences automatically. It has three main steps: 1) mapping each protein sequence into a matrix built on all kinds of adjacent amino acids; 2) applying the low-rank approximation model to the obtained matrix to solve its lowest rank representation, which reflects its true subspace structures; and 3) utilizing a powerful kernel extreme learning machine to predict the probability for PPI based on this lowest rank representation. Experimental results on a large-scale human PPI dataset demonstrate that the proposed LELM has significant advantages in accuracy and efficiency over the state-of-art approaches. Hence, this work establishes a new and effective way for the automatic detection of PPI.

Original languageEnglish (US)
Article number7444177
Pages (from-to)731-743
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number3
StatePublished - Mar 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Big data
  • feature extraction
  • kernel extreme learning machine (K-ELM)
  • low-rank approximation (LRA)
  • protein-protein interactions (PPIs)
  • support vector machine (SVM)


Dive into the research topics of 'Highly Efficient Framework for Predicting Interactions Between Proteins'. Together they form a unique fingerprint.

Cite this