  Protein-Protein Interactions (PPIs) perform essential roles in biological
functions. Although some experimental techniques have been developed to detect
PPIs, they suffer from high false positive and high false negative rates.
Consequently, efforts have been devoted during recent years to develop
computational approaches to predict the interactions utilizing various sources
of information. Therefore, a unique category of prediction approaches has been
devised which is based on the protein sequence information. However, finding an
appropriate feature encoding to characterize the sequence of proteins is a
major challenge in such methods. In presented work, a sequence based method is
proposed to predict protein-protein interactions using N-Gram encoding
approaches to describe amino acids and a Relaxed Variable Kernel Density
Estimator (RVKDE) as a machine learning tool. Moreover, since proteins can
rotate in 3D-space, amino acid compositions have been considered with
"undirected" property which leads to reduce dimensions of the vector space. The
results show that our proposed method achieves the superiority of prediction
performance with improving an F-measure of 2.5% on Human Protein Reference
Dataset (HPRD).
