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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Descriptor: Benchmarking Secure Neural Network Evaluation Methods for Protein Sequence Classification (iDASH24).

Arif Harmanci1, Luyao Chen1, Miran Kim2

  • 1Department of Health Data Science and Artificial Intelligence, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030 USA.

IEEE Data Descriptions
|December 23, 2024
PubMed
Summary
This summary is machine-generated.

The iDASH24 dataset enables standardized testing and benchmarking of secure evaluation for transformer-based models using homomorphic encryption. This resource aids in developing and assessing privacy-preserving machine learning strategies for genomic data.

Keywords:
Genomic privacyhomomorphic encryption (HE)transformer model

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Area of Science:

  • Computational biology
  • Cryptography
  • Machine learning

Background:

  • Transformer models are increasingly used in bioinformatics, raising privacy concerns for sensitive genomic data.
  • Secure evaluation of these models is crucial for privacy-preserving analysis.
  • Existing benchmarks for secure model evaluation are limited.

Purpose of the Study:

  • To introduce the iDASH24 homomorphic encryption track dataset for benchmarking secure evaluation of transformer models.
  • To provide a standardized resource for testing privacy-preserving machine learning techniques.
  • To facilitate the development of secure genomic data analysis methods.

Main Methods:

  • Designed a dataset comprising a protein family classification transformer model and associated example data.
  • Utilized homomorphic encryption schemes for secure model evaluation.
  • Organized the iDASH24 Genomic Privacy Competition to test secure evaluation strategies.

Main Results:

  • The iDASH24 dataset was successfully used in a competition setting for secure evaluation of a transformer model.
  • Benchmarking results and companion methods were generated during the competition.
  • The dataset facilitated the exploration of homomorphic encryption for secure machine learning.

Conclusions:

  • The iDASH24 dataset is a valuable resource for benchmarking secure evaluation of neural network models, particularly transformers.
  • It supports the advancement of privacy-preserving machine learning in genomics.
  • Facilitates standardized testing of homomorphic encryption applications in AI.