Dataset Information

Identifying tinnitus-related genes based on a side-effect network analysis.

ABSTRACT: Tinnitus, phantom sound perception, is a worldwide highly prevalent disorder for which no clear underlying pathology has been established and for which no approved drug is on the market. Thus, there is an urgent need for new approaches to understand this condition. We used a network pharmacology side-effect analysis to search for genes that are involved in tinnitus generation. We analyzed a network of 1,313 drug-target pairs, based on 275 compounds that elicit tinnitus as side effect and their targets reported in databases, and used a quantitative score to identify emergent significant targets that were more common than expected at random. Cyclooxigenase 1 and 2 were significant, which validates our approach, since salicylate is a known tinnitus generator. More importantly, we predict previously unknown tinnitus-related targets. The present results have important implications toward understanding tinnitus pathophysiology and might pave the way toward the design of novel pharmacotherapies.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e97; doi:10.1038/psp.2013.75; published online 29 January 2014.

SUBMITTER: Elgoyhen AB

PROVIDER: S-EPMC3910011 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying tinnitus-related genes based on a side-effect network analysis.

Elgoyhen A B AB Langguth B B Nowak W W Schecklmann M M De Ridder D D Vanneste S S

CPT: pharmacometrics & systems pharmacology 20140129

Tinnitus, phantom sound perception, is a worldwide highly prevalent disorder for which no clear underlying pathology has been established and for which no approved drug is on the market. Thus, there is an urgent need for new approaches to understand this condition. We used a network pharmacology side-effect analysis to search for genes that are involved in tinnitus generation. We analyzed a network of 1,313 drug-target pairs, based on 275 compounds that elicit tinnitus as side effect and their t ...[more]

PMID: 24477090

Similar Datasets

Project description:Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Dataset Information

Identifying tinnitus-related genes based on a side-effect network analysis.

Publications

Identifying tinnitus-related genes based on a side-effect network analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets