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Department of Information Systems and Security

College of Information Technology


Mining Biological Networks towards Protein-Complex Detection and Gene-Disease Association

Faculty Advisor

Prof. Nazar Zaki

Defense Date

30 September 2015


Large amounts of biological data are continuously generated nowadays, thanks to the advancements of

high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the

design of suitable computational methods, to complement the experimental work which is often restricted

by considerable time and cost requirements. Protein complexes, or groups of interacting proteins, are

key players in most cellular events. The identification of complexes not only allows to better understand

normal biological processes but also to uncover disease-triggering malfunctions. Ultimately, findings in this

research branch can highly enhance the design of effective medical treatments. The aim of this research

is to detect protein complexes in protein-protein interaction networks and to associate the detected

entities to diseases. The work is divided into three main objectives: first, develop a suitable method for

the identification of protein complexes in static interaction networks; second, model the dynamic aspect

of protein interaction networks and detect complexes accordingly; and third, design a learning model

to link proteins, and subsequently protein complexes, to diseases. In response to these objectives, we

present, ProRank+, a novel complex-detection approach based on a ranking algorithm and a merging

procedure. Then, we introduce DyCluster, which uses gene expression data, to model the dynamics of

the interaction networks, and we adapt the detection algorithm accordingly. Finally, we integrate network

topology attributes and several biological features of proteins to form a classification model for gene-

disease association. The reliability of the proposed methods is supported by various experimental studies

conducted to compare them with existing approaches. ProRank+ detects more protein complexes than

other state-of-the-art methods. DyCluster goes a step further and achieves a better performance than

similar techniques. Then, our learning model shows that combining topological and biological features can

greatly enhance the gene-disease association process. Finally, we present a comprehensive case study of

breast cancer in which we pinpoint disease genes using our learning model; and we subsequently detect

favorable groupings of those genes in a protein interaction network using the ProRank+ algorithm.


Feb 12, 2017
Dec 13, 2017