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Amanda Araújo Serrão de Andrade1, Marco Grivet2, Otávio Brustolini1

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The study introduces (m, n)-mer frequencies as a superior classification feature over k-mers. This new method enhances performance in sequence classification tasks, especially for shorter sequences and smaller k values.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Traditional k-mer frequency features are widely used for sequence classification.
  • Limitations exist in k-mer performance, particularly with complex datasets and shorter sequences.

Purpose of the Study:

  • To compare the performance of (m, n)-mer frequency features against traditional k-mer features.
  • To evaluate the effectiveness of (m, n)-mers in various classification tasks including binary, multiclass, and clustering.
  • To introduce (m, n)-mer frequencies as a potentially more powerful feature for sequence analysis.

Main Methods:

  • Utilized 11 distinct biological datasets for comparative analysis.
  • Implemented and compared k-mer and (m, n)-mer frequency feature extraction methods.
  • Assessed performance metrics across binary, multiclass, and clustering classification scenarios.

Main Results:

  • (m, n)-mer frequency features demonstrated superior performance across multiple datasets and classification types.
  • Statistically significant improvements were observed when using (m, n)-mers compared to k-mers.
  • (m, n)-mers particularly excelled in classifying shorter sequences (down to 300 bp) and with smaller k values (2-4).

Conclusions:

  • (m, n)-mer frequencies are an effective feature for identifying complex discriminatory patterns in biological sequences.
  • This method offers enhanced classification accuracy, especially for challenging datasets and sequence lengths.
  • The (m, n)-mer algorithm is available as an R package and on GitHub for community use.