Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/59881
Title: Logic Network Modelling of cancer signalling pathways
Authors: Srinidhi Rajanarayanan
Keywords: DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis
Issue Date: 2014
Abstract: The 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.
URI: http://hdl.handle.net/10356/59881
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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