Scalable and Efficient Protein Secondary Structure Prediction Using Autoencoder-Reduced ProtBERT Embeddings

Scalable and Efficient Protein Secondary Structure Prediction Using Autoencoder-Reduced ProtBERT Embeddings

This study proposes a deep learning framework for Protein Secondary Structure Prediction (PSSP) that prioritizes computational efficiency while preserving classification accuracy. Leveraging ProtBERT-derived embeddings, we apply autoencoder-based dimensionality reduction to compress high-dimensional...

Full description

Saved in:
Bibliographic Details
Main Authors: Yahya Najib Hamood Al-Shameri, İrfan Kösesoy, Hakan Gündüz, Ömer Faruk Yılmaz
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
protein secondary structure prediction
ProtBERT
representation learning
autoencoders
Bi-LSTM
Online Access:https://www.mdpi.com/2076-3417/15/13/7112
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items