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Proteins associated with multiple diseases often interact, forming disease modules that are critical for understanding disease mechanisms. This study integrates protein-protein interactions (PPIs) and Gene Ontology data using non-negative matrix factorization (NMF) to identify gene modules associated with human diseases. We leverage two biological sources of information, protein-protein interactions (PPIs) and Gene Ontology data, to find connections between novel genes and diseases. The data sources are first converted into networks, which are then clustered to obtain modules. Two types of modules are then integrated through an NMF-based technique to obtain a set of meta-modules that preserve the essential characteristics of interaction patterns and functional similarity information among the proteins/genes. Each meta-module is labeled based on its statistical and biological properties, and a multi-label classification technique is employed to assign new disease labels to genes. We identified 3,131 gene-disease associations, validated through a literature review, Gene Ontology, and pathway analysis.

[This corrects the article DOI: 10.1371/journal.pone.0332794.].

The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experimentally verified viral–host interaction data (SARS-CoV-2–human interactions published on April 30, 2020) provide an invaluable resource, these datasets include only a limited number of high-confidence interactions. Here, we extend these resources using a deep learning–based multiview graph neural network approach, coupled with optimal transport–based integration. Our comprehensive validation strategy confirms 472 high-confidence predicted interactions between 280 host proteins and 27 SARS-CoV-2 proteins. The proposed model demonstrates robust predictive performance, achieving ROC-AUC scores of 85.9% (PPI network), 83.5% (GO similarity network), and 83.1% (sequence similarity network), with corresponding average precision scores of 86.4%, 82.8%, and 82.3% on independent test sets. Comparative evaluation shows that our multiview approach consistently outperforms conventional single-view and baseline graph learning methods. The model combines features derived from protein sequences, gene ontology terms, and physical interaction information to improve interaction prediction. Furthermore, we systematically map the predicted host factors to FDA-approved drugs and identify several candidates, including lenalidomide and pirfenidone, which have established or emerging roles in COVID-19 therapy. Overall, our framework provides comprehensive and accurate predictions of SARS-CoV-2–host protein interactions and represents a valuable resource for drug repurposing efforts.

The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experimentally verified viral-host interaction data (SARS-CoV-2-human interactions published on April 30, 2020) provide an invaluable resource, these datasets include only a limited number of high-confidence interactions. Here, we extend these resources using a deep learning-based multiview graph neural network approach, coupled with optimal transport-based integration. Our comprehensive validation strategy confirms 472 high-confidence predicted interactions between 280 host proteins and 27 SARS-CoV-2 proteins. The proposed model demonstrates robust predictive performance, achieving ROC-AUC scores of 85.9% (PPI network), 83.5% (GO similarity network), and 83.1% (sequence similarity network), with corresponding average precision scores of 86.4%, 82.8%, and 82.3% on independent test sets. Comparative evaluation shows that our multiview approach consistently outperforms conventional single-view and baseline graph learning methods. The model combines features derived from protein sequences, gene ontology terms, and physical interaction information to improve interaction prediction. Furthermore, we systematically map the predicted host factors to FDA-approved drugs and identify several candidates, including lenalidomide and pirfenidone, which have established or emerging roles in COVID-19 therapy. Overall, our framework provides comprehensive and accurate predictions of SARS-CoV-2-host protein interactions and represents a valuable resource for drug repurposing efforts.

Extensive evidence recognizes that proteins associated with several diseases frequently interact with each other. This leads to develop different network-based methods for uncovering the molecular workings of human diseases. These methods are based on the idea that protein interaction networks act as maps, where diseases manifest as localized perturbations within a neighborhood. Identifying these areas, known as disease modules, is essential for in-depth research into specific disease characteristics. While many computational methods have been developed the underlying connectivity patterns within these modules still yet to be explored. This work aim to fill this gap by integrating multiple biological data sources through non-negative matrix factorization (NMF) technique. We leverage two biological sources of information, protein-protein interactions (PPIs) and Gene Ontology data to find connections between novel genes and diseases. The data sources are first converted into networks, which are then clustered to obtain modules. Two types of modules are then integrated through NMF-based technique to obtain a set of meta-modules which preserve the essential characteristics of interaction patterns and functional similarity information among the proteins/genes. We assign multiple labels to each meta-module based on the statistical and biological properties they shared with the disease dataset. A multi-label classification technique is utilized to assign new disease labels to genes within each meta-modules. A total of 3131 gene-disease associations are identified, which are also validated through a literature survey, gene ontology and pathway-based analysis.