A Comprehensive Survey of Tools and Software for Active Subnetwork Identification
A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-03-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00155/full |
| _version_ | 1818313662041948160 |
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| author | Hung Nguyen Sangam Shrestha Duc Tran Adib Shafi Sorin Draghici Sorin Draghici Tin Nguyen |
| author_facet | Hung Nguyen Sangam Shrestha Duc Tran Adib Shafi Sorin Draghici Sorin Draghici Tin Nguyen |
| author_sort | Hung Nguyen |
| collection | DOAJ |
| description | A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose. |
| first_indexed | 2024-12-13T08:37:18Z |
| format | Article |
| id | doaj.art-a25fe38d384f473c80955bca9bb50154 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-13T08:37:18Z |
| publishDate | 2019-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-a25fe38d384f473c80955bca9bb501542022-12-21T23:53:36ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-03-011010.3389/fgene.2019.00155426749A Comprehensive Survey of Tools and Software for Active Subnetwork IdentificationHung Nguyen0Sangam Shrestha1Duc Tran2Adib Shafi3Sorin Draghici4Sorin Draghici5Tin Nguyen6Department of Computer Science and Engineering, University of Nevada, Reno, NV, United StatesDepartment of Computer Science and Engineering, University of Nevada, Reno, NV, United StatesDepartment of Computer Science and Engineering, University of Nevada, Reno, NV, United StatesDepartment of Computer Science, Wayne State University, Detroit, MI, United StatesDepartment of Computer Science, Wayne State University, Detroit, MI, United StatesDepartment of Obstetrics and Gynecology, Wayne State University, Detroit, MI, United StatesDepartment of Computer Science and Engineering, University of Nevada, Reno, NV, United StatesA recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.https://www.frontiersin.org/article/10.3389/fgene.2019.00155/fullactive moduleactive subnetworksubnetwork identificationdata integrationPPI networknetwork analysis |
| spellingShingle | Hung Nguyen Sangam Shrestha Duc Tran Adib Shafi Sorin Draghici Sorin Draghici Tin Nguyen A Comprehensive Survey of Tools and Software for Active Subnetwork Identification Frontiers in Genetics active module active subnetwork subnetwork identification data integration PPI network network analysis |
| title | A Comprehensive Survey of Tools and Software for Active Subnetwork Identification |
| title_full | A Comprehensive Survey of Tools and Software for Active Subnetwork Identification |
| title_fullStr | A Comprehensive Survey of Tools and Software for Active Subnetwork Identification |
| title_full_unstemmed | A Comprehensive Survey of Tools and Software for Active Subnetwork Identification |
| title_short | A Comprehensive Survey of Tools and Software for Active Subnetwork Identification |
| title_sort | comprehensive survey of tools and software for active subnetwork identification |
| topic | active module active subnetwork subnetwork identification data integration PPI network network analysis |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00155/full |
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