INBIA: a boosting methodology for proteomic network inference
Abstract Background The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cance...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-07-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2183-5 |
| _version_ | 1828963118625062912 |
|---|---|
| author | Davide S. Sardina Giovanni Micale Alfredo Ferro Alfredo Pulvirenti Rosalba Giugno |
| author_facet | Davide S. Sardina Giovanni Micale Alfredo Ferro Alfredo Pulvirenti Rosalba Giugno |
| author_sort | Davide S. Sardina |
| collection | DOAJ |
| description | Abstract Background The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. Results We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. Conclusion Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions. |
| first_indexed | 2024-12-14T10:18:46Z |
| format | Article |
| id | doaj.art-7295d4e9dc6b4adc9edfb9fc4f23aeaa |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T10:18:46Z |
| publishDate | 2018-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-7295d4e9dc6b4adc9edfb9fc4f23aeaa2022-12-21T23:06:40ZengBMCBMC Bioinformatics1471-21052018-07-0119S7778810.1186/s12859-018-2183-5INBIA: a boosting methodology for proteomic network inferenceDavide S. Sardina0Giovanni Micale1Alfredo Ferro2Alfredo Pulvirenti3Rosalba Giugno4Department of Computer Science, University of VeronaDepartment of Mathematics and Computer Science, University of CataniaDepartment of Clinical and Experimental Medicine, University of CataniaDepartment of Clinical and Experimental Medicine, University of CataniaDepartment of Computer Science, University of VeronaAbstract Background The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. Results We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. Conclusion Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.http://link.springer.com/article/10.1186/s12859-018-2183-5Protein interaction networkNetwork inferenceProtein expressionNetwork algorithm |
| spellingShingle | Davide S. Sardina Giovanni Micale Alfredo Ferro Alfredo Pulvirenti Rosalba Giugno INBIA: a boosting methodology for proteomic network inference BMC Bioinformatics Protein interaction network Network inference Protein expression Network algorithm |
| title | INBIA: a boosting methodology for proteomic network inference |
| title_full | INBIA: a boosting methodology for proteomic network inference |
| title_fullStr | INBIA: a boosting methodology for proteomic network inference |
| title_full_unstemmed | INBIA: a boosting methodology for proteomic network inference |
| title_short | INBIA: a boosting methodology for proteomic network inference |
| title_sort | inbia a boosting methodology for proteomic network inference |
| topic | Protein interaction network Network inference Protein expression Network algorithm |
| url | http://link.springer.com/article/10.1186/s12859-018-2183-5 |
| work_keys_str_mv | AT davidessardina inbiaaboostingmethodologyforproteomicnetworkinference AT giovannimicale inbiaaboostingmethodologyforproteomicnetworkinference AT alfredoferro inbiaaboostingmethodologyforproteomicnetworkinference AT alfredopulvirenti inbiaaboostingmethodologyforproteomicnetworkinference AT rosalbagiugno inbiaaboostingmethodologyforproteomicnetworkinference |