Feature fusion with attributed deepwalk for protein-protein interaction prediction.

ABSTRACT: protein-protein interactions (PPIs) are crucial for understanding cellular processes and disease mechanisms. While experimental methods for detecting PPIs exist, computational approaches offer a more efficient alternative. However, current computational methods often rely on single feature types or simple feature concatenation, potentially missing the complex nature of protein interactions. This study proposes FFADW (Feature Fusion Method with Attributed DeepWalk), a novel approach that integrates sequence and network features using a weighted fusion strategy controlled by an adjustable α parameter. Specifically, sequence similarity is computed using Levenshtein distance, while network similarity is measured via a Gaussian kernel-based approach. These complementary features are fused throu

SUBMITTER: Cao MY 

PROVIDER: S-EPMC11985984 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature