MultiFusion2HPO: A Multimodal Deep Learning Approach for Enhancing Human Protein-Phenotype Association Prediction

Accurately identifying associations between human genes (proteins) and clinical phenotypes is critical for advancing drug development and precision medicine. While the human phenotype ontology (HPO) standardizes clinical phenotypes, current computational approaches for predicting human protein-phenotype associations suffer from two limitations: (1) underutilization of multimodal protein-related information and (2) lack of state-of-the-art deep learning representations tailored to diverse data modalities, such as text and sequence. To overcome these limitations, we introduce MultiFusion2HPO, a novel multimodal model that integrates diverse features and advanced learning methods from multiple data sources to enhance the prediction of human protein-HPO associations. MultiFusion2HPO leverages five critical modalities: textual information (TFIDF-D2V and BioLinkBERT embeddings), protein sequence data (InterPro and ESM2), protein-protein interaction (PPI) networks, gene ontology (GO) annotation, and gene expression. Comprehensive experiments on benchmark datasets demonstrate the superiority of MultiFusion2HPO over the state-of-the-art methods, DeepPheno and HPOLabeler. These results underscore the effectiveness of integrating multimodal protein data to improve the accuracy of human protein-HPO association predictions.