• BioPrediction-PPI: predicting interactions between biological sequences

Proteins play crucial roles in various biological processes by interacting with other molecules, primarily other proteins. These interactions are essential for cellular pathways and life maintenance. Predicting protein-protein interactions (PPIs) is challenging but vital for understanding cellular functions and disease mechanisms. To address this challenge, this article proposes BioPrediction-PPI, an end-to-end framework for predicting protein-protein interactions. The objective is to provide a tool that simplifies predicting PPIs by automating an end-to-end Machine Learning (ML) pipeline, eliminating the need for manual intervention. BioPrediction-PPI automates the entire ML process, from feature extraction to interpretability, ensuring a user-friendly experience accessible to researchers without specialized expertise. The performance of BioPrediction-PPI was evaluated through comparative experiments with other tools mentioned in the literature, using different datasets. Some experiments follow methodologies similar to those used in previous studies. The results indicate a favorable trend for BioPrediction-PPI, demonstrating competitiveness against 21 other ML models. Notably, BioPrediction-PPI outperforms the standard model in 8 out of 8 human-virus datasets. Therefore, its user-friendly nature and competitive performance make it a promising tool for democratizing ML model building in biology and related fields. This accessibility and effectiveness could significantly accelerate research and development in understanding protein-protein interactions, benefiting biological research and related applications.