ReviewNature reviews. Genetics2012
Computational tools for prioritizing candidate genes: boosting disease gene discovery.
Review in Nature reviews. Genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 213 papers, 3 of them syntheses that pooled it.
What it found
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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
213 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Meta-Analysis and Bioinformatics Detection of Susceptibility Genes in Diabetic Nephropathy.International journal of molecular sciences · 2021Pooled it
- Common variants in the MKL1 gene confer risk of schizophrenia.Schizophrenia bulletin · 2015Pooled it
- Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes.Schizophrenia bulletin · 2014Pooled it
- Comparative DCMS analysis reveals a novel breed-specific X-linked selection signature in Changthangi sheep.Frontiers in genetics · 2026Article
- A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- From gene modules to gene markers: an integrated AI-human approach selects CD38 to represent plasma cell-associated transcriptional signatures.Frontiers in medicine · 2025Article
- Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).BMC bioinformatics · 2024Article
- STIGMA: Single-cell tissue-specific gene prioritization using machine learning.American journal of human genetics · 2024Article
- Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.Scientific reports · 2023Article
- Evolutionary history of MEK1 illuminates the nature of deleterious mutations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.PLoS computational biology · 2023Article
- MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication.PLoS computational biology · 2023Article
- Combinatorial Network of Transcriptional and miRNA Regulation in Colorectal Cancer.International journal of molecular sciences · 2023Article
- Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits.American journal of human genetics · 2023Article
- Detecting biased validation of predictive models in the positive-unlabeled setting: disease gene prioritization case study.Bioinformatics advances · 2023Article
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases.Biomolecules · 2022Article
- Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.Genes · 2022Review
- Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease.PLoS genetics · 2022Article
153 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
Indexed as
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22751426What 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.