Show simple item record

dc.contributor.authorWu, Q
dc.contributor.authorSmith-Miles, K
dc.contributor.authorTian, T
dc.date.accessioned2020-12-17T03:51:56Z
dc.date.available2020-12-17T03:51:56Z
dc.date.issued2014-11-06
dc.identifierpii: 1471-2105-15-S12-S3
dc.identifier.citationWu, Q., Smith-Miles, K. & Tian, T. (2014). Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density. BMC BIOINFORMATICS, 15 (12), https://doi.org/10.1186/1471-2105-15-S12-S3.
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/11343/255065
dc.description.abstractBACKGROUND: Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic. RESULTS: To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate Bayesian computation. Two stochastic models were used to demonstrate the efficiency and effectiveness of the proposed method. In addition, we designed another algorithm based on a novel objective function to measure the accuracy of stochastic simulations. CONCLUSIONS: Simulation results suggest that the usage of simulated likelihood density improves the accuracy of estimates substantially. When the error is measured at each observation time point individually, the estimated parameters have better accuracy than those obtained by a published method in which the error is measured using simulations over the entire observation time period.
dc.languageEnglish
dc.publisherBMC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleApproximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density
dc.typeJournal Article
dc.identifier.doi10.1186/1471-2105-15-S12-S3
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.source.titleBMC Bioinformatics
melbourne.source.volume15
melbourne.source.issue12
dc.rights.licenseCC BY
melbourne.elementsid1231709
melbourne.contributor.authorSmith-Miles, Kate
dc.identifier.eissn1471-2105
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record