دورية أكاديمية
ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization
| العنوان: | ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization |
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| المؤلفون: | Chen Ye, Qi Wu, Shuxia Chen, Xuemei Zhang, Wenwen Xu, Yunzhi Wu, Youhua Zhang, Yi Yue |
| المصدر: | BMC Genomics, Vol 25, Iss 1, Pp 1-23 (2024) |
| بيانات النشر: | BMC, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Biotechnology LCC:Genetics |
| مصطلحات موضوعية: | Essential protein, Evolutionary community discovery, Protein–protein interaction network, Subcellular localization, Gene expression, Biotechnology, TP248.13-248.65, Genetics, QH426-470 |
| الوصف: | Abstract Background In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. Results We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein–protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. Conclusions Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2164 |
| العلاقة: | https://doaj.org/toc/1471-2164Test |
| DOI: | 10.1186/s12864-024-10019-5 |
| الوصول الحر: | https://doaj.org/article/d83859d6356b4607a6736b67fbcc3a72Test |
| رقم الانضمام: | edsdoj.83859d6356b4607a6736b67fbcc3a72 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14712164 |
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| DOI: | 10.1186/s12864-024-10019-5 |