FUSE: a profit maximization approach for functional summarization of biological networks
Background: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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BioMed Central Ltd.
2012
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| Online Access: | http://hdl.handle.net/1721.1/69918 https://orcid.org/0000-0001-7387-3572 |
| _version_ | 1811071607468195840 |
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| author | Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes Yu, Hanry |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes Yu, Hanry |
| author_sort | Seah, Boon-Siew |
| collection | MIT |
| description | Background: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results: We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion: By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment. |
| first_indexed | 2024-09-23T08:53:51Z |
| format | Article |
| id | mit-1721.1/69918 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:53:51Z |
| publishDate | 2012 |
| publisher | BioMed Central Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/699182022-09-30T11:58:41Z FUSE: a profit maximization approach for functional summarization of biological networks Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes Yu, Hanry Massachusetts Institute of Technology. Department of Biological Engineering Dewey, C. Forbes Background: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results: We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion: By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment. 2012-04-04T14:08:25Z 2012-04-04T14:08:25Z 2012-03 2012-03-21T13:47:20Z Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/69918 Seah, Boon-Siew et al. “FUSE: a profit maximization approach for functional summarization of biological networks.” BMC Bioinformatics 13.Suppl 3 (2012): S10. https://orcid.org/0000-0001-7387-3572 en http://dx.doi.org/10.1186/1471-2105-13-S3-S10 BMC Bioinformatics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd. BioMed Central Ltd |
| spellingShingle | Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes Yu, Hanry FUSE: a profit maximization approach for functional summarization of biological networks |
| title | FUSE: a profit maximization approach for functional summarization of biological networks |
| title_full | FUSE: a profit maximization approach for functional summarization of biological networks |
| title_fullStr | FUSE: a profit maximization approach for functional summarization of biological networks |
| title_full_unstemmed | FUSE: a profit maximization approach for functional summarization of biological networks |
| title_short | FUSE: a profit maximization approach for functional summarization of biological networks |
| title_sort | fuse a profit maximization approach for functional summarization of biological networks |
| url | http://hdl.handle.net/1721.1/69918 https://orcid.org/0000-0001-7387-3572 |
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