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dc.contributor.advisorRavasi, Timothy
dc.contributor.authorAlanis Lobato, Gregorio
dc.date.accessioned2014-06-29T13:31:04Z
dc.date.available2014-06-29T13:31:04Z
dc.date.issued2014-06
dc.identifier.doi10.25781/KAUST-46D6F
dc.identifier.urihttp://hdl.handle.net/10754/322302
dc.description.abstractThe network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.
dc.language.isoen
dc.subjectlink prediction
dc.subjectbio-networks
dc.subjectsystems biology
dc.subjectnetwork science
dc.subjectdimensionality reduction
dc.subjectnetwork embedding
dc.titleExploitation of complex network topology for link prediction in biological interactomes
dc.typeDissertation
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberGao, Xin
dc.contributor.committeememberSolovyev, Victor
dc.contributor.committeememberMoshkov, Mikhail
dc.contributor.committeememberBatzoglou, Serafim
thesis.degree.disciplineComputer Science
thesis.degree.nameDoctor of Philosophy
refterms.dateFOA2018-06-14T07:03:29Z


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