pSuc-EDBAM

pSuc-EDBAM predicts lysine succinylation sites in protein sequences by extracting and prioritizing features using ensemble dense blocks with an attention module and one-hot encoded 1-D CNN feature maps to support studies of protein function and disease pathways.

Key Features:

• Ensemble Dense Blocks and Attention Module: Captures intricate succinylation-related sequence patterns and prioritizes informative features.
• One-Hot Encoding and 1-D CNN: Converts protein sequences into one-hot representations and generates low-level feature maps via 1-D convolution.
• Multi-Layered Learning Strategy: Applies stacked ensemble dense-block learning for hierarchical feature extraction and representation.

Scientific Applications:

• Lysine Succinylation Research: Predicts potential lysine succinylation sites to aid investigations of protein function and disease pathways.

Methodology:

Represents sequences with one-hot encoding, produces feature maps using a 1-D CNN, and applies ensemble dense blocks with an attention module within a multi-layered learning strategy for feature extraction and prioritization.