ArticleBMC medical genomics2013
Integrating human omics data to prioritize candidate genes.
Article in BMC medical genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- GDC: Integration of Multi-Omic and Phenotypic Resources to Unravel the Genetic Pathogenesis of Hearing Loss.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Mowat-Wilson Syndrome: Case Report and Review ofInternational journal of molecular sciences · 2024Review
- Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.JMIR formative research · 2023Article
- An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices.Axioms · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Multidimensional molecular measurements-environment interaction analysis for disease outcomes.Biometrics · 2022Article
- Evaluation and comparison of multi-omics data integration methods for cancer subtyping.PLoS computational biology · 2021Article
- Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus.Frontiers in genetics · 2021Article
- Horizontal and vertical integrative analysis methods for mental disorders omics data.Scientific reports · 2019Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- Constructing an integrated gene similarity network for the identification of disease genes.Journal of biomedical semantics · 2017Article
- Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data.National science review · 2016Article
- RASopathies: Presentation at the Genome, Interactome, and Phenome Levels.Molecular syndromology · 2016Article
- Correlating information contents of gene ontology terms to infer semantic similarity of gene products.Computational and mathematical methods in medicine · 2014Article
- Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Integrative analysis of multiple diverse omics datasets by sparse group multitask regression.Frontiers in cell and developmental biology · 2014Article
- Approaches for recognizing disease genes based on network.BioMed research international · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of genes involved in human complex diseases remains a great challenge in computational systems biology. Although methods have been developed to use disease phenotypic similarities with a protein-protein interaction network for the prioritization of candidate genes, other valuable omics data sources have been largely overlooked in these methods.
methodsWith this understanding, we proposed a method called BRIDGE to prioritize candidate genes by integrating disease phenotypic similarities with such omics data as protein-protein interactions, gene sequence similarities, gene expression patterns, gene ontology annotations, and gene pathway memberships. BRIDGE utilizes a multiple regression model with lasso penalty to automatically weight different data sources and is capable of discovering genes associated with diseases whose genetic bases are completely unknown.
resultsWe conducted large-scale cross-validation experiments and demonstrated that more than 60% known disease genes can be ranked top one by BRIDGE in simulated linkage intervals, suggesting the superior performance of this method. We further performed two comprehensive case studies by applying BRIDGE to predict novel genes and transcriptional networks involved in obesity and type II diabetes.
conclusionThe proposed method provides an effective and scalable way for integrating multi omics data to infer disease genes. Further applications of BRIDGE will be benefit to providing novel disease genes and underlying mechanisms of human diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.