ICLRBBN

ICLRBBN predicts potential associations between long non-coding RNAs (lncRNAs) and human diseases using internal confidence-based collaborative filtering and a three-layer local radial basis function network to quantify association probabilities.

Key Features:

• Internal Confidence-Based Collaborative Filtering: Employs an internal confidence-based collaborative filtering recommendation algorithm to mine hidden features between lncRNAs and diseases.
• Three-Layer Local Radial Basis Function Network: Implements a three-layer local radial basis function network that integrates lncRNA characteristics and localized disease information to calculate association probabilities.
• Superior Predictive Performance: Validated against six state-of-the-art methods using two different frameworks and demonstrates higher area under the receiver operating characteristic curve (AUC) values.

Scientific Applications:

• lncRNA–disease association prediction: Prioritizes candidate lncRNA-disease associations for downstream experimental validation.
• Biological network analysis: Supports exploration of lncRNA-related biological networks underlying complex diseases.
• Therapeutic target identification: Aids identification of novel insights and candidate therapeutic targets linked to lncRNA-disease relationships.

Methodology:

The method mines hidden features using an internal confidence-based collaborative filtering recommendation algorithm and constructs a three-layer local radial basis function network that integrates lncRNA characteristics with localized disease data to calculate association probabilities.