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MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.

Robson P Bonidia1, Douglas S Domingues2, Danilo S Sanches3

  • 1Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil.

Briefings in Bioinformatics
|November 9, 2021
PubMed
Summary
This summary is machine-generated.

A new package, MathFeature, offers novel mathematical descriptors for biological sequence data (DNA, RNA, proteins). These descriptors enhance machine learning applications by providing robust feature extraction, outperforming existing methods in benchmark studies.

Keywords:
GUI-based platformbiological sequencesfeature extractionmathematical descriptorspackagepython

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Machine learning on biological sequences requires effective numerical representation.
  • Existing feature extraction methods are often not readily available in standard packages.
  • Mathematical descriptors are crucial for capturing sequence information.

Purpose of the Study:

  • Introduce MathFeature, a novel package for extracting numerical features from biological sequences.
  • Implement 20 mathematical descriptors based on diverse computational approaches.
  • Enhance machine learning applications in genomics and proteomics.

Main Methods:

  • Developed the MathFeature package implementing 20 mathematical descriptors.
  • Utilized approaches including numeric mappings, genomic signal processing, chaos game theory, entropy, and complex networks.
  • Validated descriptor robustness and relevance through nine case studies and eight benchmark datasets.

Main Results:

  • MathFeature descriptors achieved high performance (0.6350-0.9897 accuracy) in case studies.
  • Features extracted by MathFeature outperformed existing methods in benchmark datasets.
  • Hybridization of MathFeature descriptors with known methods further improved performance.

Conclusions:

  • MathFeature provides unique, robust mathematical descriptors for biological sequence analysis.
  • The package enhances machine learning model performance for DNA, RNA, and protein sequences.
  • MathFeature democratizes advanced feature extraction for researchers, including non-experts.