Development of machine learning approaches based on biological networks for predic...
| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Estadual Paulista, UNESP, Inst Biociencias Botucatu, Dept Fis & Biofis, BR-18618970 Sao Paulo - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | BMC Genomics; v. 13, AUG 17 2012. |
| Web of Science Citations: | 114 |
| Abstract | |
Background: The modeling of interactions among transcription factors (TFs) and their respective target genes (TGs) into transcriptional regulatory networks is important for the complete understanding of regulation of biological processes. In the case of experimentally verified human TF-TG interactions, there is no database at present that explicitly provides such information even though many databases containing human TF-TG interaction data have been available. In an effort to provide researchers with a repository of experimentally verified human TF-TG interactions from which such interactions can be directly extracted, we present here the Human Transcriptional Regulation Interactions database (HTRIdb). Description: The HTRIdb is an open-access database that can be searched via a user-friendly web interface and the retrieved TF-TG interactions data and the associated protein-protein interactions can be downloaded or interactively visualized as a network through the web version of the popular Cytoscape visualization tool, the Cytoscape Web. Moreover, users can improve the database quality by uploading their own interactions and indicating inconsistencies in the data. So far, HTRIdb has been populated with 284 TFs that regulate 18302 genes, totaling 51871 TF-TG interactions. HTRIdb is freely available at http://www.lbbc.ibb.unesp.br/htri. Conclusions: HTRIdb is a powerful user-friendly tool from which human experimentally validated TF-TG interactions can be easily extracted and used to construct transcriptional regulation interaction networks enabling researchers to decipher the regulation of biological processes. (AU) | |
| FAPESP's process: | 10/20684-3 - Development of machine learning approaches based on biological networks for prediction and determination of rules governing the emergence of phenotypes of interest |
| Grantee: | Marcio Luis Acencio |
| Support type: | Scholarships in Brazil - Post-Doctorate |
| FAPESP's process: | 09/10382-2 - Machine learning for molecular systems biology |
| Grantee: | Ney Lemke |
| Support type: | Regular Research Grants |