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Protein classification using modified n-grams and skip-grams.

S M Ashiqul Islam1, Benjamin J Heil2, Christopher Michel Kearney1,3

  • 1Institute of Biomedical Studies.

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|January 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Natural Language Processing (NLP) model, modified n-grams and skip-grams (m-NGSG), to automate protein feature generation for supervised machine learning classification, improving accuracy and accessibility.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Supervised machine learning aids protein characteristic annotation from primary sequences.
  • Feature generation in protein classification requires domain expertise and can lead to irrelevant feature selection, compromising model accuracy.

Purpose of the Study:

  • To introduce a supervised protein classification method that automates feature generation.
  • To address the limitations of manual feature engineering in protein sequence analysis.

Main Methods:

  • Developed a Natural Language Processing (NLP)-dependent model using a modified combination of n-grams and skip-grams (m-NGSG).
  • Automated the feature generation step in supervised protein classification.

Main Results:

  • The m-NGSG model demonstrated a consistent increase in classification accuracy compared to contemporary methods.
  • Meta-comparison across twelve datasets from nine studies validated the superior performance of m-NGSG.

Conclusions:

  • The m-NGSG model accelerates protein classification from primary sequence data.
  • This approach enhances the accessibility of protein characteristic prediction for a wider scientific audience.