In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network
Recent functional genomics research has yielded a large in silico gene regulatory network model (622 nodes) for endomesoderm development of sea urchin, a model organism for embryonic development. The size of this network makes it challenging to determine which genes are most responsible for a given...
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sg-ntu-dr.10356-1074352020-05-28T07:17:57Z In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network Bhowmick, Sourav S. Chua, Huey-Eng Tucker-Kellogg, Lisa Zhao, Qing Dewey Jr., C. Forbes Yu, Hanry School of Computer Engineering International Health Informatics Symposium (2nd : 2012 : Miami, USA) DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences Recent functional genomics research has yielded a large in silico gene regulatory network model (622 nodes) for endomesoderm development of sea urchin, a model organism for embryonic development. The size of this network makes it challenging to determine which genes are most responsible for a given biological effect. In this paper, we explore feasibility and accuracy of existing in silico techniques for identifying key genes that regulate Endo16, a widely-accepted gastrulation marker. We apply target prioritization tools (sensitivity analysis and PANI) to the endomesoderm network to identify key regulators of Endo16 and validate the results by comparing against a set of benchmark Endo16 regulators collated from literature survey. Our study reveals that global sensitivity analysis methods are prohibitively expensive and inappropriate for large networks. We show that PANI efficiently produces superior prioritization results compared to both random prioritization and local sensitivity analysis (LSA) techniques. Specifically, the area under the ROC curve was 0.625, ~0.5, and 0.549 for PANI, random prioritization, and LSA, respectively. Our study reveals that certain unique characteristics of the endomesoderm network affect the performance of target prioritization techniques. In addition to identifying many known regulators of Endo16, PANI also discovered additional regulators (e.g., Snail) that did not appear initially in the benchmark regulators set. 2013-10-21T09:04:52Z 2019-12-06T22:31:01Z 2013-10-21T09:04:52Z 2019-12-06T22:31:01Z 2012 2012 Conference Paper Chua, H.-E., Bhowmick, S. S., Tucker-Kellogg, L., Zhao, Q., Dewey, C. F., & Yu, H. (2012). In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp131-140. https://hdl.handle.net/10356/107435 http://hdl.handle.net/10220/16671 10.1145/2110363.2110381 en |
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DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences Bhowmick, Sourav S. Chua, Huey-Eng Tucker-Kellogg, Lisa Zhao, Qing Dewey Jr., C. Forbes Yu, Hanry In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
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Recent functional genomics research has yielded a large in silico gene regulatory network model (622 nodes) for endomesoderm development of sea urchin, a model organism for embryonic development. The size of this network makes it challenging to determine which genes are most responsible for a given biological effect. In this paper, we explore feasibility and accuracy of existing in silico techniques for identifying key genes that regulate Endo16, a widely-accepted gastrulation marker. We apply target prioritization tools (sensitivity analysis and PANI) to the endomesoderm network to identify key regulators of Endo16 and validate the results by comparing against a set of benchmark Endo16 regulators collated from literature survey. Our study reveals that global sensitivity analysis methods are prohibitively expensive and inappropriate for large networks. We show that PANI efficiently produces superior prioritization results compared to both random prioritization and local sensitivity analysis (LSA) techniques. Specifically, the area under the ROC curve was 0.625, ~0.5, and 0.549 for PANI, random prioritization, and LSA, respectively. Our study reveals that certain unique characteristics of the endomesoderm network affect the performance of target prioritization techniques. In addition to identifying many known regulators of Endo16, PANI also discovered additional regulators (e.g., Snail) that did not appear initially in the benchmark regulators set. |
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School of Computer Engineering |
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School of Computer Engineering Bhowmick, Sourav S. Chua, Huey-Eng Tucker-Kellogg, Lisa Zhao, Qing Dewey Jr., C. Forbes Yu, Hanry |
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Conference or Workshop Item |
author |
Bhowmick, Sourav S. Chua, Huey-Eng Tucker-Kellogg, Lisa Zhao, Qing Dewey Jr., C. Forbes Yu, Hanry |
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Bhowmick, Sourav S. |
title |
In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
title_short |
In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
title_full |
In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
title_fullStr |
In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
title_full_unstemmed |
In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
title_sort |
in silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network |
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2013 |
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https://hdl.handle.net/10356/107435 http://hdl.handle.net/10220/16671 |
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1681056235720278016 |