STRIKE: A protein-protein interaction classification approach

Nazar Zaki, Wassim El-Hajj, Hesham M. Kamel, Fadi Sibai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Protein-protein interaction has proven to be a valuable biological knowledge and an initial point for understanding how the cell internally works. In this chapter, we introduce a novel approach termed STRIKE which uses String Kernel to predict protein-protein interaction. STRIKE classifies protein pairs into "interacting" and "non-interacting" sets based solely on amino acid sequence information. The classification is performed by applying the string kernel approach, which has been shown to achieve good performance on text categorization and protein sequence classification. Two proteins are classified as "interacting" if they contain similar substrings of amino acids. Strings' similarity would allow one to infer homology which could lead to a very similar structural relationship. To evaluate the performance of STRIKE, we apply it to classify into "interacting" and "non-interacting" protein pairs. The dataset of the protein pairs are generated from the yeast protein interaction literature. The dataset is supported by different lines of experimental evidence. STRIKE was able to achieve reasonable improvement over the existing protein-protein interaction prediction methods.

Original languageEnglish
Title of host publicationSoftware Tools and Algorithms for Biological Systems
EditorsHamid Arabnia, Quoc-Nam Tran
Number of pages8
Publication statusPublished - 2011

Publication series

NameAdvances in Experimental Medicine and Biology
ISSN (Print)0065-2598


  • Amino acid sequencing
  • Biological data mining and knowledge discovery
  • Pattern classification and recognition
  • Protein sequence analysis
  • Protein-protein interaction

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology


Dive into the research topics of 'STRIKE: A protein-protein interaction classification approach'. Together they form a unique fingerprint.

Cite this