ArticleBMC systems biology2014
Prediction of disease genes using tissue-specified gene-gene network.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Strategies for constructing context-specific protein-protein interaction networks.Briefings in bioinformatics · 2026Review
- Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network.BMC medical genomics · 2019Article
- GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion.International journal of biological sciences · 2019Article
- Predicting circRNA-Disease Associations Based on Improved Collaboration Filtering Recommendation System With Multiple Data.Frontiers in genetics · 2019Article
- Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.Frontiers in genetics · 2019Article
- Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network.Journal, genetic engineering & biotechnology · 2018Article
- Predicting multicellular function through multi-layer tissue networks.Bioinformatics (Oxford, England) · 2017Article
- Predicting disease-related genes using integrated biomedical networks.BMC genomics · 2017Article
- Pancreatic Islet Protein Complexes and Their Dysregulation in Type 2 Diabetes.Frontiers in genetics · 2017Article
- Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks.BMC bioinformatics · 2016Article
- Molecular networks in context.Nature biotechnology · 2015Article
- A fast and high performance multiple data integration algorithm for identifying human disease genes.BMC medical genomics · 2015Article
- ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity.BioMed research international · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTissue specificity is an important aspect of many genetic diseases in the context of genetic disorders as the disorder affects only few tissues. Therefore tissue specificity is important in identifying disease-gene associations. Hence this paper seeks to discuss the impact of using tissue specificity in predicting new disease-gene associations and how to use tissue specificity along with phenotype information for a particular disease.
methodsIn order to find out the impact of using tissue specificity for predicting new disease-gene associations, this study proposes a novel method called tissue-specified genes to construct tissues-specific gene-gene networks for different tissue samples. Subsequently, these networks are used with phenotype details to predict disease genes by using Katz method. The proposed method was compared with three other tissue-specific network construction methods in order to check its effectiveness. Furthermore, to check the possibility of using tissue-specific gene-gene network instead of generic protein-protein network at all time, the results are compared with three other methods.
resultsIn terms of leave-one-out cross validation, calculation of the mean enrichment and ROC curves indicate that the proposed approach outperforms existing network construction methods. Furthermore tissues-specific gene-gene networks make a more positive impact on predicting disease-gene associations than generic protein-protein interaction networks.
conclusionsIn conclusion by integrating tissue-specific data it enabled prediction of known and unknown disease-gene associations for a particular disease more effectively. Hence it is better to use tissue-specific gene-gene network whenever possible. In addition the proposed method is a better way of constructing tissue-specific gene-gene networks.
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