Related Experiment Video
Updated: Jun 4, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
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.
Abstract:
To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.
More Related Videos
09:34A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
08:31Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Related Concept Videos
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces
Conserved Binding Sites
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...