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  3. Learning Peptide Properties With Positive Examples Only.
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  3. Learning Peptide Properties With Positive Examples Only.

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Learning peptide properties with positive examples only.

Mehrad Ansari1, Andrew D White1

  • 1Department of Chemical Engineering, University of Rochester Rochester NY 14627 USA andrew.white@rochester.edu.

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|May 17, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel semi-supervised deep learning approach using positive-unlabeled learning for peptide discovery. This method effectively predicts antimicrobial peptide properties using only positive examples, overcoming data limitations.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning

Background:

  • Deep learning models require extensive labeled data, including negative examples, which are scarce for peptide properties.
  • High-throughput screening methods struggle to efficiently generate negative examples for peptide databases.

Purpose of the Study:

  • To develop a semi-supervised deep learning framework for predicting peptide properties using limited positive examples.
  • To address the challenge of missing negative data in peptide sequence analysis.

Main Methods:

  • Implemented positive-unlabeled (PU) learning strategies: adapting base classifiers and reliable negative identification.
  • Built deep learning models to infer peptide solubility, hemolysis, SHP-2 binding, and non-fouling activity from sequences.
  • Evaluated predictive performance against traditional positive-negative (PN) classification.

    • Main Results:

    • The PU learning method achieved competitive predictive performance using only positive peptide data.
    • Demonstrated the efficacy of PU learning in scenarios with limited negative examples.
    • Successfully predicted multiple antimicrobial peptide properties.

    Conclusions:

    • Semi-supervised PU learning is a viable alternative to PN learning when negative data is scarce.
    • This approach enables effective peptide property prediction and guides molecule design.
    • Advances the application of deep learning in antimicrobial peptide research.