Predicting host-pathogen interactions with machine learning algorithms: A scoping review

Abstract

Background

Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protein-protein interactions (PPIs), is crucial for developing therapeutic strategies. While experimental approaches are essential, they are often labor-intensive and costly. Researchers have been able to predict HPIs more efficiently due to recent advances in artificial intelligence and machine learning. However, existing reviews lack a systematic evaluation of different machine learning methodologies and their effectiveness.

Methods

This scoping review critically examines recent studies on machine learning-based Host-Pathogen Interaction (HPI) prediction, categorizing them by host and pathogen types, machine learning algorithms, and key evaluation metrics. The methodology is based on the study beginning with a preliminary search in reputable using key phrases related to host-pathogen interactions from 2019 to 2024. This process yielded 46 relevant articles, from which 30 were selected for review after evaluating titles and abstracts.

Results

Our findings indicate that tree-based algorithms, particularly Random Forest and Gradient Boosting, are the most prevalent in Host-Pathogen Interaction (HPI) prediction. The filter articles were categorized by host and pathogen type and further subdivided into four subcategories based on the prediction type and machine learning algorithms: classic, tree-based, vector-based, and neural network algorithms. Convolutional and recurrent neural networks are among the deep learning models that demonstrate promising accuracy, but they require a lot of labeled data for effective training. Additionally, the analysis uncovers significant gaps in dataset standardization and model interpretability, which pose challenges to the broader applicability of these predictive models.

Conclusion

In this review, we emphasize the potential of machine learning in HPI prediction and highlight the important challenges that must be addressed to improve predictive accuracy. Unlike previous reviews, our study systematically compares different computational approaches, offering a roadmap for future research. The findings emphasize the importance of dataset quality, feature selection, and model transparency in advancing AI-driven pathogen research.