Please use this identifier to cite or link to this item: https://doi.org/10.1049/iet-syb.2012.0011
DC FieldValue
dc.titleGenetic programming-based approach to elucidate biochemical interaction networks from data
dc.contributor.authorKandpal, M.
dc.contributor.authorKalyan, C.M.
dc.contributor.authorSamavedham, L.
dc.date.accessioned2014-10-09T06:48:49Z
dc.date.available2014-10-09T06:48:49Z
dc.date.issued2013
dc.identifier.citationKandpal, M., Kalyan, C.M., Samavedham, L. (2013). Genetic programming-based approach to elucidate biochemical interaction networks from data. IET Systems Biology 7 (1) : 18-25. ScholarBank@NUS Repository. https://doi.org/10.1049/iet-syb.2012.0011
dc.identifier.issn17518849
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/89001
dc.description.abstractBiochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods. © The Institution of Engineering and Technology 2013.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1049/iet-syb.2012.0011
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentSINGAPORE-DELFT WATER ALLIANCE
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1049/iet-syb.2012.0011
dc.description.sourcetitleIET Systems Biology
dc.description.volume7
dc.description.issue1
dc.description.page18-25
dc.identifier.isiut000321863900003
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