Mining the modular structure of protein interaction networks
Background: Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithm...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | JOUR |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_19326203_v10_n4_p_Berenstein |
| Aporte de: |
| id |
todo:paper_19326203_v10_n4_p_Berenstein |
|---|---|
| record_format |
dspace |
| spelling |
todo:paper_19326203_v10_n4_p_Berenstein2023-10-03T16:34:36Z Mining the modular structure of protein interaction networks Berenstein, A.J. Piñero, J. Furlong, L.I. Chernomoretz, A. Article classification algorithm controlled study gene cluster gene expression genetic transcription molecular recognition process optimization protein analysis protein assembly protein interaction protein structure structure analysis aging algorithm cluster analysis data mining gene regulatory network genetics human protein protein interaction statistics and numerical data Aging Algorithms Cluster Analysis Data Mining Gene Regulatory Networks Humans Protein Interaction Mapping Protein Interaction Maps Background: Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. Methodology: We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. Results: As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge. © 2015 Berenstein et al. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_19326203_v10_n4_p_Berenstein |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Article classification algorithm controlled study gene cluster gene expression genetic transcription molecular recognition process optimization protein analysis protein assembly protein interaction protein structure structure analysis aging algorithm cluster analysis data mining gene regulatory network genetics human protein protein interaction statistics and numerical data Aging Algorithms Cluster Analysis Data Mining Gene Regulatory Networks Humans Protein Interaction Mapping Protein Interaction Maps |
| spellingShingle |
Article classification algorithm controlled study gene cluster gene expression genetic transcription molecular recognition process optimization protein analysis protein assembly protein interaction protein structure structure analysis aging algorithm cluster analysis data mining gene regulatory network genetics human protein protein interaction statistics and numerical data Aging Algorithms Cluster Analysis Data Mining Gene Regulatory Networks Humans Protein Interaction Mapping Protein Interaction Maps Berenstein, A.J. Piñero, J. Furlong, L.I. Chernomoretz, A. Mining the modular structure of protein interaction networks |
| topic_facet |
Article classification algorithm controlled study gene cluster gene expression genetic transcription molecular recognition process optimization protein analysis protein assembly protein interaction protein structure structure analysis aging algorithm cluster analysis data mining gene regulatory network genetics human protein protein interaction statistics and numerical data Aging Algorithms Cluster Analysis Data Mining Gene Regulatory Networks Humans Protein Interaction Mapping Protein Interaction Maps |
| description |
Background: Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. Methodology: We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. Results: As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge. © 2015 Berenstein et al. |
| format |
JOUR |
| author |
Berenstein, A.J. Piñero, J. Furlong, L.I. Chernomoretz, A. |
| author_facet |
Berenstein, A.J. Piñero, J. Furlong, L.I. Chernomoretz, A. |
| author_sort |
Berenstein, A.J. |
| title |
Mining the modular structure of protein interaction networks |
| title_short |
Mining the modular structure of protein interaction networks |
| title_full |
Mining the modular structure of protein interaction networks |
| title_fullStr |
Mining the modular structure of protein interaction networks |
| title_full_unstemmed |
Mining the modular structure of protein interaction networks |
| title_sort |
mining the modular structure of protein interaction networks |
| url |
http://hdl.handle.net/20.500.12110/paper_19326203_v10_n4_p_Berenstein |
| work_keys_str_mv |
AT berensteinaj miningthemodularstructureofproteininteractionnetworks AT pineroj miningthemodularstructureofproteininteractionnetworks AT furlongli miningthemodularstructureofproteininteractionnetworks AT chernomoretza miningthemodularstructureofproteininteractionnetworks |
| _version_ |
1807320739106258944 |