RP-FIRF: Prediction of Self-interacting Proteins Using Random Projection Classifier Combining with Finite Impulse Response Filter

  • Zhan-Heng Chen
  • Zhu-Hong YouEmail author
  • Li-Ping Li
  • Yan-Bin Wang
  • Xiao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


The self-interacting proteins (SIPs) plays a significant part in the organism and the regulation of cellular functions. Thence, we developed an effective algorithm to predict SIPs, named RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, the Position Specific Scoring Matrix (PSSM) was firstly converted from protein sequence by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, we obtained the same size of matrix by implementing a valid matrix multiplication on PSSM, and applied FIRF approach to calculate the eigenvalues of each protein. The Principal Component Analysis (PCA) approach is used to extract the most relevant information. Finally, the performance of the proposed method is performed on human dataset. The results show that our model can achieve high average accuracies of 97.89% on human dataset using the 5-fold cross-validation, which demonstrate that our method is a useful tool for identifying SIPs.


Self-interacting proteins Random projection Finite impulse response filter 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zhan-Heng Chen
    • 1
    • 2
  • Zhu-Hong You
    • 1
    • 2
    Email author
  • Li-Ping Li
    • 1
  • Yan-Bin Wang
    • 1
    • 2
  • Xiao Li
    • 1
  1. 1.The Xinjiang Technical Institute of Physics and ChemistryChinese Academy of SciencesUrumqiChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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