Assessment of network module identification across complex diseases
<p dir="ltr">Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different type...
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2019
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| _version_ | 1864513515230855168 |
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| author | Sarvenaz Choobdar (18602821) |
| author2 | The DREAM Module Identification Challenge Consortium (18602824) Mehmet E. Ahsen (18602827) Jake Crawford (14846212) Mattia Tomasoni (4227274) Tao Fang (434629) David Lamparter (606695) Junyuan Lin (13017369) Benjamin Hescott (475948) Xiaozhe Hu (18602830) Johnathan Mercer (18602833) Ted Natoli (1339764) Rajiv Narayan (4635193) Aravind Subramanian (712720) Jitao D. Zhang (14913645) Gustavo Stolovitzky (239041) Zoltán Kutalik (174641) Kasper Lage (42180) Donna K. Slonim (14636480) Julio Saez-Rodriguez (25229) Lenore J. Cowen (18602836) Sven Bergmann (20384) Daniel Marbach (366952) |
| author2_role | author author author author author author author author author author author author author author author author author author author author author author |
| author_facet | Sarvenaz Choobdar (18602821) The DREAM Module Identification Challenge Consortium (18602824) Mehmet E. Ahsen (18602827) Jake Crawford (14846212) Mattia Tomasoni (4227274) Tao Fang (434629) David Lamparter (606695) Junyuan Lin (13017369) Benjamin Hescott (475948) Xiaozhe Hu (18602830) Johnathan Mercer (18602833) Ted Natoli (1339764) Rajiv Narayan (4635193) Aravind Subramanian (712720) Jitao D. Zhang (14913645) Gustavo Stolovitzky (239041) Zoltán Kutalik (174641) Kasper Lage (42180) Donna K. Slonim (14636480) Julio Saez-Rodriguez (25229) Lenore J. Cowen (18602836) Sven Bergmann (20384) Daniel Marbach (366952) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sarvenaz Choobdar (18602821) The DREAM Module Identification Challenge Consortium (18602824) Mehmet E. Ahsen (18602827) Jake Crawford (14846212) Mattia Tomasoni (4227274) Tao Fang (434629) David Lamparter (606695) Junyuan Lin (13017369) Benjamin Hescott (475948) Xiaozhe Hu (18602830) Johnathan Mercer (18602833) Ted Natoli (1339764) Rajiv Narayan (4635193) Aravind Subramanian (712720) Jitao D. Zhang (14913645) Gustavo Stolovitzky (239041) Zoltán Kutalik (174641) Kasper Lage (42180) Donna K. Slonim (14636480) Julio Saez-Rodriguez (25229) Lenore J. Cowen (18602836) Sven Bergmann (20384) Daniel Marbach (366952) |
| dc.date.none.fl_str_mv | 2019-08-30T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41592-019-0509-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Assessment_of_network_module_identification_across_complex_diseases/25886893 |
| 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 Bioinformatics gene networks protein networks module identification disease relevance DREAM Challenge |
| dc.title.none.fl_str_mv | Assessment of network module identification across complex diseases |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.</p><h2>Other Information</h2><p dir="ltr">Published in: Nature Methods<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41592-019-0509-5" target="_blank">https://dx.doi.org/10.1038/s41592-019-0509-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f077f637abd2c4a92275ee773e327e26 |
| identifier_str_mv | 10.1038/s41592-019-0509-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25886893 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Assessment of network module identification across complex diseasesSarvenaz Choobdar (18602821)The DREAM Module Identification Challenge Consortium (18602824)Mehmet E. Ahsen (18602827)Jake Crawford (14846212)Mattia Tomasoni (4227274)Tao Fang (434629)David Lamparter (606695)Junyuan Lin (13017369)Benjamin Hescott (475948)Xiaozhe Hu (18602830)Johnathan Mercer (18602833)Ted Natoli (1339764)Rajiv Narayan (4635193)Aravind Subramanian (712720)Jitao D. Zhang (14913645)Gustavo Stolovitzky (239041)Zoltán Kutalik (174641)Kasper Lage (42180)Donna K. Slonim (14636480)Julio Saez-Rodriguez (25229)Lenore J. Cowen (18602836)Sven Bergmann (20384)Daniel Marbach (366952)Biological sciencesBioinformatics and computational biologyBioinformaticsgene networksprotein networksmodule identificationdisease relevanceDREAM Challenge<p dir="ltr">Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.</p><h2>Other Information</h2><p dir="ltr">Published in: Nature Methods<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41592-019-0509-5" target="_blank">https://dx.doi.org/10.1038/s41592-019-0509-5</a></p>2019-08-30T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41592-019-0509-5https://figshare.com/articles/journal_contribution/Assessment_of_network_module_identification_across_complex_diseases/25886893CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/258868932019-08-30T03:00:00Z |
| spellingShingle | Assessment of network module identification across complex diseases Sarvenaz Choobdar (18602821) Biological sciences Bioinformatics and computational biology Bioinformatics gene networks protein networks module identification disease relevance DREAM Challenge |
| status_str | publishedVersion |
| title | Assessment of network module identification across complex diseases |
| title_full | Assessment of network module identification across complex diseases |
| title_fullStr | Assessment of network module identification across complex diseases |
| title_full_unstemmed | Assessment of network module identification across complex diseases |
| title_short | Assessment of network module identification across complex diseases |
| title_sort | Assessment of network module identification across complex diseases |
| topic | Biological sciences Bioinformatics and computational biology Bioinformatics gene networks protein networks module identification disease relevance DREAM Challenge |