dc.contributor.authorSrinidhi Rajanarayanan
dc.date.accessioned2014-05-19T02:43:53Z
dc.date.available2014-05-19T02:43:53Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/59881
dc.description.abstractThe purpose of the project is to reconstruct Boolean models of signaling. Conventionally, large-scale protein-protein interactions were viewed as static models. Recently functional models of these networks have been suggested ranging from Boolean to constraint-based models. Most of these models rely on extensive human curation thereby making it difficult to learn these models from large data sets. The primary intention of the paper is to infer Boolean models of signaling, automatically from data. The approach is applied to growth and inflammatory signaling systems in human and show how the learning phase can improve the fit of the model to experimental data, remove spurious interactions and lead to better understanding of the system at hand.en_US
dc.format.extent58 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysisen_US
dc.titleLogic Network Modelling of cancer signalling pathwaysen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorZheng Jieen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
dc.contributor.researchBioinformatics Research Centreen_US


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