PSO-FeatureFusion: a general framework for fusing heterogeneous features via particle swarm optimization.

ABSTRACT:

Motivation

Integrating heterogeneous biological data is a central challenge in bioinformatics, especially when modeling complex relationships among entities such as drugs, diseases, and molecular features. Existing methods often rely on static or separate feature extraction processes, which may fail to capture interactions across diverse feature types and reduce predictive accuracy.

Results

To address these limitations, we propose PSO-FeatureFusion, a unified framework that combines particle swarm optimization with neural networks to jointly integrate and optimize features from multiple biological entities. By modeling pairwise feature interactions and learning their optimal contributions, the framework captures individual feature signals and their interdependencies in a tas

SUBMITTER: Masumshah R 

PROVIDER: S-EPMC12596698 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature