Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion

Area of Science:

• Genomics
• Environmental Health
• Computational Biology

Background:

• Environmental factors (EFs) are known to influence microRNA (miRNA) expression.
• Dysregulation of miRNA is implicated in the pathogenesis of numerous diseases.
• Understanding miRNA-EF associations is crucial for disease mechanism elucidation.

Purpose of the Study:

• To propose a novel computational method, MEI-BRWMLL, for identifying associations between microRNAs and environmental factors.
• To enhance the accuracy of predicting miRNA-EF interactions.

Main Methods:

• Calculating miRNA-miRNA similarity using sequence and interaction data.
• Calculating EF-EF similarity based on anatomical therapeutic chemical information, chemical structure, and interaction data.
• Employing similarity network fusion, multiple-label learning, and bi-random walk for association identification.

Main Results:

• The proposed MEI-BRWMLL method demonstrates superior performance compared to existing state-of-the-art algorithms.
• Validation of the method's efficacy in uncovering significant miRNA-EF relationships.

Conclusions:

• MEI-BRWMLL provides an effective computational approach for discovering miRNA-EF associations.
• This method can significantly contribute to understanding environmentally influenced diseases.