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Protein fold recognition based on sparse representation based classification.

Ke Yan1, Yong Xu1, Xiaozhao Fang1

  • 1School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, China.

Artificial Intelligence in Medicine
|April 1, 2017
PubMed
Summary
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This study introduces Sparse Representation based Classification (SRC) for protein fold recognition, improving prediction accuracy. The novel MF-SRC predictor combines multiple features within SRC for enhanced performance and noise reduction in protein structure analysis.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural bioinformatics

Background:

  • Protein fold type is crucial for determining protein structure and function.
  • Existing machine learning methods like SVMs and ANNs have limitations in protein fold recognition.
  • Novel computational techniques are needed to enhance predictive performance.

Purpose of the Study:

  • To apply Sparse Representation based Classification (SRC) to the protein fold recognition problem.
  • To improve upon existing machine learning methods for predicting protein fold types.
  • To develop a novel computational predictor, MF-SRC, for enhanced fold recognition.

Main Methods:

  • Utilizing Sparse Representation based Classification (SRC) for protein fold recognition.
Keywords:
Protein fold recognitionProtein representationSparse representation based classification

Related Experiment Videos

  • Integrating three feature selection methods: autocross-covariance (ACC) fold, D-D, and Bi-gram.
  • Developing the MF-SRC predictor by combining features within the SRC framework.

    • Main Results:

    • SRC improves the performance of basic classifiers and state-of-the-art feature selection methods.
    • The MF-SRC predictor demonstrates improved performance on benchmark datasets (DD, EDD, TG).
    • The proposed method shows stable performance and reduces the influence of noise in datasets.

    Conclusions:

    • SRC is a promising technique for protein fold recognition.
    • The MF-SRC predictor offers enhanced accuracy and stability compared to existing methods.
    • MF-SRC can serve as a valuable high-throughput tool for large-scale fold recognition.