TPVOD is a full Python package implementation of the research presented in the paper "Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species." This project aims to examine the evolution of miRNA–target interaction rules and assess their cross-species transferability using data science and machine learning (ML) approaches.

MicroRNAs (miRNAs) are crucial in regulating gene expression post-transcriptionally. This package focuses on the challenges and possibilities in the ML-based target prediction for miRNA, particularly across different species where direct experimental data might be scarce.

• High accuracy in intra-dataset classification of miRNA–target interactions across human, mouse, worm, and cattle.
• Significant overlap in the most influential features across all datasets.
• The transferability of miRNA–targeting rules correlates with the evolutionary distance and seed family composition.

1. Clone the repository: git clone https://github.com/gbenor/TPVOD.
2. Install required Python packages: pip install -r requirements.txt.

• Detailed instructions on how to utilize this package for analyzing miRNA–target interactions, including example scripts and data processing guidelines.

Contributions to TPVOD are welcome.Please send me pull requests :-)

This project is freely licensed under the MIT License.

This implementation is a tribute to the authors of the original paper, providing a computational framework to extend their groundbreaking work to non-model organisms and furthering the field of miRNA research.

For academic use, please cite the original paper: Comprehensive machine-learning-based analysis of microRNA–target interactions.