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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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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| author | Hongyi Zhou Brice Edelman Jeffrey Skolnick |
| author_facet | Hongyi Zhou Brice Edelman Jeffrey Skolnick |
| author_sort | Hongyi Zhou |
| collection | DOAJ |
| description | 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/ . |
| format | Article |
| id | doaj-art-ec4a623caf9b46489ca2a4f3903ab01b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ec4a623caf9b46489ca2a4f3903ab01b2025-08-20T03:41:40ZengNature PortfolioScientific Reports2045-23222025-03-0115111710.1038/s41598-025-93377-8A mode of action protein based approach that characterizes the relationships among most major diseasesHongyi Zhou0Brice Edelman1Jeffrey Skolnick2Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyCenter for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyCenter for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyAbstract 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/ .https://doi.org/10.1038/s41598-025-93377-8 |
| spellingShingle | Hongyi Zhou Brice Edelman Jeffrey Skolnick A mode of action protein based approach that characterizes the relationships among most major diseases Scientific Reports |
| title | A mode of action protein based approach that characterizes the relationships among most major diseases |
| title_full | A mode of action protein based approach that characterizes the relationships among most major diseases |
| title_fullStr | A mode of action protein based approach that characterizes the relationships among most major diseases |
| title_full_unstemmed | A mode of action protein based approach that characterizes the relationships among most major diseases |
| title_short | A mode of action protein based approach that characterizes the relationships among most major diseases |
| title_sort | mode of action protein based approach that characterizes the relationships among most major diseases |
| url | https://doi.org/10.1038/s41598-025-93377-8 |
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