Detection of statistically significant network changes in complex biological networks
<h3>Background</h3><p dir="ltr">Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses,...
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| مؤلفون آخرون: | , , , |
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2017
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| _version_ | 1864513556590886912 |
|---|---|
| author | Raghvendra Mall (581171) |
| author2 | Luigi Cerulo (447376) Halima Bensmail (10400) Antonio Iavarone (3814138) Michele Ceccarelli (184154) |
| author2_role | author author author author |
| author_facet | Raghvendra Mall (581171) Luigi Cerulo (447376) Halima Bensmail (10400) Antonio Iavarone (3814138) Michele Ceccarelli (184154) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raghvendra Mall (581171) Luigi Cerulo (447376) Halima Bensmail (10400) Antonio Iavarone (3814138) Michele Ceccarelli (184154) |
| dc.date.none.fl_str_mv | 2017-03-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12918-017-0412-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Detection_of_statistically_significant_network_changes_in_complex_biological_networks/27087997 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Differential networks Gene regulatory network inference Master regulators |
| dc.title.none.fl_str_mv | Detection of statistically significant network changes in complex biological networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks.</p><h3>Methods</h3><p dir="ltr">In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate <i>p</i>-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation.</p><h3>Results</h3><p dir="ltr">In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.</p><h3>Conclusions</h3><p dir="ltr">We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Systems Biology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12918-017-0412-6" target="_blank">https://dx.doi.org/10.1186/s12918-017-0412-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d8dcd3794a0dae09a0bf37356223b825 |
| identifier_str_mv | 10.1186/s12918-017-0412-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27087997 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Detection of statistically significant network changes in complex biological networksRaghvendra Mall (581171)Luigi Cerulo (447376)Halima Bensmail (10400)Antonio Iavarone (3814138)Michele Ceccarelli (184154)Biological sciencesBioinformatics and computational biologyDifferential networksGene regulatory network inferenceMaster regulators<h3>Background</h3><p dir="ltr">Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks.</p><h3>Methods</h3><p dir="ltr">In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate <i>p</i>-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation.</p><h3>Results</h3><p dir="ltr">In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.</p><h3>Conclusions</h3><p dir="ltr">We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Systems Biology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12918-017-0412-6" target="_blank">https://dx.doi.org/10.1186/s12918-017-0412-6</a></p>2017-03-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12918-017-0412-6https://figshare.com/articles/journal_contribution/Detection_of_statistically_significant_network_changes_in_complex_biological_networks/27087997CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270879972017-03-01T00:00:00Z |
| spellingShingle | Detection of statistically significant network changes in complex biological networks Raghvendra Mall (581171) Biological sciences Bioinformatics and computational biology Differential networks Gene regulatory network inference Master regulators |
| status_str | publishedVersion |
| title | Detection of statistically significant network changes in complex biological networks |
| title_full | Detection of statistically significant network changes in complex biological networks |
| title_fullStr | Detection of statistically significant network changes in complex biological networks |
| title_full_unstemmed | Detection of statistically significant network changes in complex biological networks |
| title_short | Detection of statistically significant network changes in complex biological networks |
| title_sort | Detection of statistically significant network changes in complex biological networks |
| topic | Biological sciences Bioinformatics and computational biology Differential networks Gene regulatory network inference Master regulators |