A Fractal Dimension and Empirical Mode Decomposition-Based Method for Protein Sequence Analysis

In bioinformatics, the biological functions of proteins and their interactions can often be analyzed by the similarity of their sequences. In this paper, the authors combine the fractal dimension, empirical mode decomposition (EMD), and sliding window for protein sequence comparison. First, the protein sequence is characterized and digitized into a signal, and then the signal characteristics are obtained by using EMD and fractal dimension. Each protein sequence can be decomposed into Intrinsic Mode Functions (IMFs). The fixed window’s fractal dimension is applied to each IMF and the original signal to extract the protein sequence characteristics. Experiments have shown that the feature extracted by this hybrid method is superior to the EMD method alone.

• Publication year

  2019

• Venue

  International journal of pattern recognition and artificial intelligence

• Publication date

  2019-10-17

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  DOI 10.1142/S0218001419400202
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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