Exploitation of complex network topology for link prediction in biological interactomes
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Gregorio - final dissertation
Type
DissertationAuthors
Alanis Lobato, Gregorio
Advisors
Ravasi, Timothy
Committee members
Gao, Xin
Solovyev, Victor

Moshkov, Mikhail

Batzoglou, Serafim
Program
Computer ScienceDate
2014-06Permanent link to this record
http://hdl.handle.net/10754/322302
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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.Citation
Alanis Lobato, G. (2014). Exploitation of complex network topology for link prediction in biological interactomes. KAUST Research Repository. https://doi.org/10.25781/KAUST-46D6Fae974a485f413a2113503eed53cd6c53
10.25781/KAUST-46D6F