ArticleNucleic acids research2006
Analysis of protein sequence and interaction data for candidate disease gene prediction.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction.Briefings in bioinformatics · 2026Article
- Schizophrenia in the genetic era: a review from development history, clinical features and genomic research approaches to insights of susceptibility genes.Metabolic brain disease · 2024Review
- Identifying the critical state of complex biological systems by the directed-network rank score method.Bioinformatics (Oxford, England) · 2022Article
- A graph centrality-based approach for candidate gene prediction for type 1 diabetes.Immunologic research · 2021Article
- Article
- Identification of infectious disease-associated host genes using machine learning techniques.BMC bioinformatics · 2019Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- Disease gene prediction for molecularly uncharacterized diseases.PLoS computational biology · 2019Article
- ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.Scientific reports · 2018Article
- Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data.Scientific reports · 2017Article
- Constructing an integrated gene similarity network for the identification of disease genes.Journal of biomedical semantics · 2017Article
- Predicting Abdominal Aortic Aneurysm Target Genes by Level-2 Protein-Protein Interaction.PloS one · 2015Article
- Novel therapeutics for coronary artery disease from genome-wide association study data.BMC medical genomics · 2015Article
- Virus proteins similar to human proteins as possible disturbance on human pathways.Systems and synthetic biology · 2014Article
- Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases.Bioinformatics (Oxford, England) · 2014Article
- Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.PLoS genetics · 2014Article
- Protein-protein interaction networks (PPI) and complex diseases.Gastroenterology and hepatology from bed to bench · 2014Review
- Text mining in cancer gene and pathway prioritization.Cancer informatics · 2014Review
- Identification of novel therapeutics for complex diseases from genome-wide association data.BMC medical genomics · 2014Article
- Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes.BioMed research international · 2014Article
7 more citing papers are in PubMed but not listed here.
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein-protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.
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