Show simple item record

dc.contributor.advisorQian, Xiaoning
dc.creatorWang, Yijie
dc.date.accessioned2015-10-29T19:44:00Z
dc.date.available2017-08-01T05:37:37Z
dc.date.created2015-08
dc.date.issued2015-08-12
dc.date.submittedAugust 2015
dc.identifier.urihttp://hdl.handle.net/1969.1/155541
dc.description.abstractAdvances in high-throughput techniques have enabled researchers to produce large-scale data on molecular interactions. Systematic analysis of these large-scale interactome datasets based on their graph representations has the potential to yield a better understanding of the functional organization of the corresponding biological systems. One way to chart out the underlying cellular functional organization is to identify functional modules in these biological networks. However, there are several challenges of module identification for biological networks. First, different from social and computer networks, molecules work together with different interaction patterns; groups of molecules working together may have different sizes. Second, the degrees of nodes in biological networks obey the power-law distribution, which indicates that there exist many nodes with very low degrees and few nodes with high degrees. Third, molecular interaction data contain a large number of false positives and false negatives. In this dissertation, we propose computational algorithms to overcome those challenges. To identify functional modules based on interaction patterns, we develop efficient algorithms based on the concept of block modeling. We propose a subgradient Frank-Wolfe algorithm with path generation method to identify functional modules and recognize the functional organization of biological networks. Additionally, inspired by random walk on networks, we propose a novel two-hop random walk strategy to detect fine-size functional modules based on interaction patterns. To overcome the degree heterogeneity problem, we propose an algorithm to identify functional modules with the topological structure that is well separated from the rest of the network as well as densely connected. In order to minimize the impact of the existence of noisy interactions in biological networks, we propose methods to detect conserved functional modules for multiple biological networks by integrating the topological and orthology information across different biological networks. For every algorithm we developed, we compare each of them with the state-of-the-art algorithms on several biological networks. The comparison results on the known gold standard biological function annotations show that our methods can enhance the accuracy of predicting protein complexes and protein functions.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectBiological networksen
dc.subjectModule identification for individual networksen
dc.subjectModule identification for multiple networksen
dc.subjectBlock modelingen
dc.subjectDegree heterogeneityen
dc.subjectNon-negative matrix factorizationen
dc.subjectTwo-hop Markov random walken
dc.subjectSub-gradient Frank-Wolfe algorithmen
dc.subjecten
dc.titleModule Identification for Biological Networksen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberYoon, Byung-Jun
dc.contributor.committeeMemberChamberland-Tremblay, Jean-Francois
dc.contributor.committeeMemberSze, Sing-Hoi
dc.type.materialtexten
dc.date.updated2015-10-29T19:44:00Z
local.embargo.terms2017-08-01
local.etdauthor.orcid0000-0002-7675-4971


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record