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Computational Proteomics Using Network-Based Strategies

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Title: Computational Proteomics Using Network-Based Strategies
Authors: Goh, Wen
Item Type: Thesis or dissertation
Abstract: This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.
Content Version: Open Access
Issue Date: Jun-2013
Date Awarded: Mar-2014
URI: http://hdl.handle.net/10044/1/24107
DOI: https://doi.org/10.25560/24107
Supervisor: Sergot, Marek
Sponsor/Funder: Wellcome Trust (London, England)
Funder's Grant Number: 83701/Z/07/Z
Department: Computing
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Computing PhD theses

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