PmliPred

PmliPred predicts interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in plants to support analysis of gene regulatory mechanisms.

Key Features:

• Hybrid Modeling Approach: Integrates a deep learning model trained on raw sequence data with a shallow machine learning model using manually extracted features.
• Fuzzy Decision Mechanism: Combines outputs from the deep and shallow models using a fuzzy decision framework to produce integrated predictions.
• Performance and Generalization: Demonstrates improved performance and generalization over existing methods, with predicted candidates validated by quantitative real-time PCR in Solanum lycopersicum (tomato).

Scientific Applications:

• Understanding Biological Mechanisms: Enables study of miRNA–lncRNA regulatory interactions that influence gene expression and plant development.
• Novel Interaction Discovery: Facilitates identification of previously unreported miRNA–lncRNA interactions in plants, with experimental validation evidence reported.

Methodology:

PmliPred trains a deep learning model on raw sequence data and a shallow machine learning model on manually extracted features, then integrates their outputs using a fuzzy decision system.