Harnessing diversity towards the reconstructing of large scale gene regulatory networks.
Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. He...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3836705?pdf=render |
| _version_ | 1811268576243351552 |
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| author | Takeshi Hase Samik Ghosh Ryota Yamanaka Hiroaki Kitano |
| author_facet | Takeshi Hase Samik Ghosh Ryota Yamanaka Hiroaki Kitano |
| author_sort | Takeshi Hase |
| collection | DOAJ |
| description | Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. |
| first_indexed | 2024-04-12T21:24:18Z |
| format | Article |
| id | doaj.art-111c0e9ca4824209ae768cdd3611c449 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T21:24:18Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-111c0e9ca4824209ae768cdd3611c4492022-12-22T03:16:12ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-01911e100336110.1371/journal.pcbi.1003361Harnessing diversity towards the reconstructing of large scale gene regulatory networks.Takeshi HaseSamik GhoshRyota YamanakaHiroaki KitanoElucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.http://europepmc.org/articles/PMC3836705?pdf=render |
| spellingShingle | Takeshi Hase Samik Ghosh Ryota Yamanaka Hiroaki Kitano Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS Computational Biology |
| title | Harnessing diversity towards the reconstructing of large scale gene regulatory networks. |
| title_full | Harnessing diversity towards the reconstructing of large scale gene regulatory networks. |
| title_fullStr | Harnessing diversity towards the reconstructing of large scale gene regulatory networks. |
| title_full_unstemmed | Harnessing diversity towards the reconstructing of large scale gene regulatory networks. |
| title_short | Harnessing diversity towards the reconstructing of large scale gene regulatory networks. |
| title_sort | harnessing diversity towards the reconstructing of large scale gene regulatory networks |
| url | http://europepmc.org/articles/PMC3836705?pdf=render |
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