Year: 2009
Type: article
Abstract: High-throughput molecular interaction data have been used effectively to prioritize candidate disease genes, based on the notion that genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs).An important challenge for these applications is the noisy nature of PPI data.Existing global methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.However, such methods favor highly connected genes, making prioritization sensitive to the degree distribution of PPI networks.Here, we propose several statistical adjustment schemes that aim to minimize these problems.While the proposed schemes are very effective in detecting loosely connected disease genes, they over-correct the highly connected genes.Motivated by these results, we develop hybrid models that integrate existing approaches with the proposed adjustment schemes.Experimental results show that proposed hybrid models significantly outperform existing methods in prioritizing disease genes.ground in the relevant issues and prepare the reader for the rest of the chapters.In Chapter 3, we formally define the problem and discuss the existing approaches based on network in depth, followed by the presentation of proposed statistical adjustment schemes.Subsequently, we define our uniform prioritization strategies using these schemes.In Chapter 4, we first introduce the experimental setup and the performance evaluation metrics used, followed by the illustration of the effect of disease similarity and statistical adjustment on the performance of existing methods.We then present the results achieved by the uniform prioritization methods we presented.Finally in Chapter 5, we conclude the work.
Author: Mehmet Sinan Erten
Institution: Case Western Reserve University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2276867980