A mode of action protein based approach that characterizes the relationships among most major diseases
Abstract Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases acr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-93377-8 |
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| Summary: | Abstract Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases across 20 clinically classified classifications (ICD10-CM major classifications). At the top level, all diseases can be classified into “infectious” and “non-infectious” diseases. Non-infectious diseases are classified into 9 classes. To demonstrate the validity of the classifications, for common pathways predicted based on MOA proteins, 77% of the top 10 most frequent pathways have literature evidence of association to their respective disease classes/subclasses. These results indicate that PICMOA will be useful for understanding common disease mechanisms and facilitating the development of drugs for a class of diseases, rather than a single disease. The MOA proteins, molecular functions, pathways for classes, and individual diseases are available at https://sites.gatech.edu/cssb/PICMOA/ . |
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| ISSN: | 2045-2322 |