Efficient experimental design for uncertainty reduction in gene regulatory networks
<h3>Background</h3><p dir="ltr">An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological expe...
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| مؤلفون آخرون: | , |
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2015
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| _version_ | 1864513557204303872 |
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| author | Roozbeh Dehghannasiri (5725805) |
| author2 | Byung-Jun Yoon (142313) Edward R Dougherty (19687015) |
| author2_role | author author |
| author_facet | Roozbeh Dehghannasiri (5725805) Byung-Jun Yoon (142313) Edward R Dougherty (19687015) |
| author_role | author |
| dc.creator.none.fl_str_mv | Roozbeh Dehghannasiri (5725805) Byung-Jun Yoon (142313) Edward R Dougherty (19687015) |
| dc.date.none.fl_str_mv | 2015-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/1471-2105-16-s13-s2 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Efficient_experimental_design_for_uncertainty_reduction_in_gene_regulatory_networks/27045157 |
| 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 Experimental design gene regulatory networks mean objective cost of uncertainty objective-based network reduction Boolean networks structural intervention |
| dc.title.none.fl_str_mv | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.</p><h3>Results</h3><p dir="ltr">The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.</p><h3>Conclusions</h3><p dir="ltr">Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly.</p><p dir="ltr">Erratum - Erratum to: Efficient experimental design for uncertainty reduction in gene regulatory networks: <a href="https://dx.doi.org/10.1186/s12859-015-0839-y" target="_blank">https://dx.doi.org/10.1186/s12859-015-0839-y</a>, published online 14 December 2015.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/1471-2105-16-s13-s2" target="_blank">https://dx.doi.org/10.1186/1471-2105-16-s13-s2</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_784731d51ab8666f1ebd8723c00ded13 |
| identifier_str_mv | 10.1186/1471-2105-16-s13-s2 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27045157 |
| publishDate | 2015 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Efficient experimental design for uncertainty reduction in gene regulatory networksRoozbeh Dehghannasiri (5725805)Byung-Jun Yoon (142313)Edward R Dougherty (19687015)Biological sciencesBioinformatics and computational biologyExperimental designgene regulatory networksmean objective cost of uncertaintyobjective-based network reductionBoolean networksstructural intervention<h3>Background</h3><p dir="ltr">An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.</p><h3>Results</h3><p dir="ltr">The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.</p><h3>Conclusions</h3><p dir="ltr">Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly.</p><p dir="ltr">Erratum - Erratum to: Efficient experimental design for uncertainty reduction in gene regulatory networks: <a href="https://dx.doi.org/10.1186/s12859-015-0839-y" target="_blank">https://dx.doi.org/10.1186/s12859-015-0839-y</a>, published online 14 December 2015.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/1471-2105-16-s13-s2" target="_blank">https://dx.doi.org/10.1186/1471-2105-16-s13-s2</a></p>2015-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/1471-2105-16-s13-s2https://figshare.com/articles/journal_contribution/Efficient_experimental_design_for_uncertainty_reduction_in_gene_regulatory_networks/27045157CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270451572015-12-01T00:00:00Z |
| spellingShingle | Efficient experimental design for uncertainty reduction in gene regulatory networks Roozbeh Dehghannasiri (5725805) Biological sciences Bioinformatics and computational biology Experimental design gene regulatory networks mean objective cost of uncertainty objective-based network reduction Boolean networks structural intervention |
| status_str | publishedVersion |
| title | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| title_full | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| title_fullStr | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| title_full_unstemmed | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| title_short | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| title_sort | Efficient experimental design for uncertainty reduction in gene regulatory networks |
| topic | Biological sciences Bioinformatics and computational biology Experimental design gene regulatory networks mean objective cost of uncertainty objective-based network reduction Boolean networks structural intervention |