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|Title:||In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network||Authors:||Bhowmick, Sourav S.
Dewey Jr., C. Forbes
|Keywords:||DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences||Issue Date:||2012||Source:||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.||Abstract:||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.||URI:||https://hdl.handle.net/10356/107435
|DOI:||10.1145/2110363.2110381||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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