In silico discovery and experimental validation of new protein-protein interactions
| العنوان: | In silico discovery and experimental validation of new protein-protein interactions |
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| المؤلفون: | Herman H. H. B. M. van Haagen, Martijn J. Schuemie, D.J.M. (Dorien) Peters, Willeke M. C. van Roon-Mom, Gert-Jan B. van Ommen, Antoine de Morrée, Marco Roos, Peter A C 't Hoen, Barend Mons |
| المساهمون: | Obstetrics & Gynecology, Medical Informatics |
| المصدر: | Proteomics, 11(5), 843-853. Wiley-VCH Proteomics Proteomics, 11(5), 843-53 van Haagen, H H H B M, 't Hoen, P A C, de Morrée, A, van Roon-Mom, W M C, Peters, D J M, Roos, M, Mons, B, van Ommen, G-J & Schuemie, M J 2011, ' In silico discovery and experimental validation of new protein-protein interactions ', Proteomics, vol. 11, no. 5, pp. 843-53 . https://doi.org/10.1002/pmic.201000398Test |
| سنة النشر: | 2010 |
| مصطلحات موضوعية: | Huntingtin, Gene Expression, Muscle Proteins, Biochemistry, Muscular Dystrophies, Dysferlin, Mice, Protein Interaction Mapping, Polycystic kidney disease, Muscular Dystrophies/genetics, Nerve Tissue Proteins/genetics, Molecular Targeted Therapy, Databases, Protein, Huntington Disease/genetics, Muscle Proteins/genetics, Genetics, Huntingtin Protein, Polycystic Kidney Diseases, String (computer science), Nuclear Proteins, Experimental validation, Polycystic Kidney Diseases/genetics, Huntington Disease, Drosophila, Algorithms, Protein Binding, Protein Interaction Mapping/methods, In silico, Protein domain, Nerve Tissue Proteins, Biology, Protein–protein interaction, SDG 3 - Good Health and Well-being, Predictive Value of Tests, medicine, Animals, Humans, RNA, Messenger, Membrane Proteins/genetics, Molecular Biology, Probability, Computational Biology, Membrane Proteins, Computational Biology/methods, medicine.disease, Protein Structure, Tertiary, Nuclear Proteins/genetics, biology.protein |
| الوصف: | We introduce a framework for predicting novel protein-protein interactions (PPIs), based on Fisher's method for combining probabilities of predictions that are based on different data sources, such as the biomedical literature, protein domain and mRNA expression information. Our method compares favorably to our previous method based on text-mining alone and other methods such as STRING. We evaluated our algorithms through the prediction of experimentally found protein interactions underlying Muscular Dystrophy, Huntington's Disease and Polycystic Kidney Disease, which had not yet been recorded in protein-protein interaction databases. We found a 1.74-fold increase in the mean average prediction precision for dysferlin and a 3.09-fold for huntingtin when compared to STRING. The top 10 of predicted interaction partners of huntingtin were analysed in depth. Five were identified previously, and the other five were new potential interaction partners. The full matrix of human protein pairs and their prediction scores are available for download. Our framework can be extended to predict other types of relationships such as proteins in a complex, pathway or related disease mechanisms. |
| تدمد: | 1615-9861 1615-9853 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e8f414fbdcc4e4342618b6a4c49c85eTest https://pubmed.ncbi.nlm.nih.gov/21280221Test |
| حقوق: | RESTRICTED |
| رقم الانضمام: | edsair.doi.dedup.....5e8f414fbdcc4e4342618b6a4c49c85e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16159861 16159853 |
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