Exploitation of complex network topology for link prediction in biological interactomes

Handle URI:
http://hdl.handle.net/10754/322302
Title:
Exploitation of complex network topology for link prediction in biological interactomes
Authors:
Alanis Lobato, Gregorio ( 0000-0001-9339-4229 )
Abstract:
The 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.
Advisors:
Ravasi, Timothy ( 0000-0002-9950-465X )
Committee Member:
Gao, Xin ( 0000-0002-7108-3574 ) ; Solovyev, Victor ( 0000-0001-8885-493X ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Batzoglou, Serafim
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
Jun-2014
Type:
Dissertation
Appears in Collections:
Dissertations; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorRavasi, Timothyen
dc.contributor.authorAlanis Lobato, Gregorioen
dc.date.accessioned2014-06-29T13:31:04Z-
dc.date.available2014-06-29T13:31:04Z-
dc.date.issued2014-06en
dc.identifier.urihttp://hdl.handle.net/10754/322302en
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.en
dc.language.isoenen
dc.subjectlink predictionen
dc.subjectbio-networksen
dc.subjectsystems biologyen
dc.subjectnetwork scienceen
dc.subjectdimensionality reductionen
dc.subjectnetwork embeddingen
dc.titleExploitation of complex network topology for link prediction in biological interactomesen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberGao, Xinen
dc.contributor.committeememberSolovyev, Victoren
dc.contributor.committeememberMoshkov, Mikhailen
dc.contributor.committeememberBatzoglou, Serafimen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameDoctor of Philosophyen
dc.person.id101969en
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