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dc.contributor.advisorCorne, David W.
dc.contributor.advisorOlsson, Bjorn
dc.contributor.authorGamalielsson, Jonas
dc.date.accessioned2010-09-03T09:38:24Z
dc.date.available2010-09-03T09:38:24Z
dc.date.issued2009-09
dc.identifier.urihttp://hdl.handle.net/10399/2294
dc.description.abstractSystems biology is an emerging multi-disciplinary field in which the behaviour of complex biological systems is studied by considering the interaction of many cellular and molecular constituents rather than using a “traditional” reductionist approach where constituents are studied individually. Systems are often studied over time with the ultimate goal of developing models which can be used to understand and predict complex biological processes, such as human diseases. To support systems biology, a large number of biological pathways are being derived for many different organisms, and these are stored in various databases. This pathway collection presents an opportunity to compare and contrast pathways, and to utilise the knowledge they represent. This thesis presents some of the first algorithms that are designed to explore this opportunity. It is argued that the methods will be useful to biologists in order to assess the biological plausibility of derived pathways, compare different biological pathways for semantic similarities, and to derive putative pathways that are semantically similar to documented biological pathways. The methods will therefore extend the systems biology toolbox that biologists can use to make new biological discoveries.en_US
dc.description.sponsorshipKnowledge Foundation. Grant No. 2003/0215en_US
dc.description.sponsorshipInformation Fusion Research Program (University of Skovde, Sweden) Grant No 2003/0104en_US
dc.language.isoenen_US
dc.publisherHeriot-Watt Universityen_US
dc.publisherMathematics and Computer Scienceen_US
dc.rightsAll items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.
dc.titleDeveloping semantic pathway comparison methods for systems biologyen_US
dc.typeThesisen_US


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