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Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins.

Xiaoyi Guo1, Prayag Tiwari2, Ying Zhang3

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, PR China.

Computers in Biology and Medicine
|November 12, 2022
PubMed
Summary

This study introduces a new machine learning model, random Fourier features-based sparse representation classifier (RFF-SRC), for accurately predicting DNA-binding proteins (DBPs). RFF-SRC offers a faster and more effective method for identifying these crucial proteins.

Keywords:
Biological sequence featuresRandom featuresSequence classificationSparse representation-based classifier

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • DNA-binding proteins (DBPs) play critical roles in DNA regulation, including protection from degradation and control of gene expression.
  • Conventional methods for DBP detection are often time-consuming and labor-intensive.
  • Machine learning approaches are emerging as efficient tools for large-scale DBP screening.

Purpose of the Study:

  • To develop an improved machine learning model for enhanced prediction of DNA-binding proteins.
  • To address the limitations of existing methods in terms of speed and accuracy for DBP identification.

Main Methods:

  • A novel classifier, random Fourier features-based sparse representation classifier (RFF-SRC), was developed.
  • Random Fourier features were employed to map data into a high-dimensional space, enabling the solution of nonlinear classification problems.
  • The L2,1-matrix norm was incorporated to achieve a sparse solution for the model.

Main Results:

  • The RFF-SRC model demonstrated superior performance compared to existing methods.
  • Experimental validation was conducted on multiple benchmark DBP datasets and 8 UCI datasets.
  • The proposed method achieved significant improvements in prediction accuracy and efficiency.

Conclusions:

  • The RFF-SRC model represents a significant advancement in the computational prediction of DNA-binding proteins.
  • This approach offers a more efficient and accurate alternative for identifying DBPs, facilitating further research in molecular biology and genomics.