ArticleHuman genomics2006
Bioinformatics methods for identifying candidate disease genes.
Article in Human genomics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed.
- Temporal and spatial omics technologies for 4D profiling.Nature methods · 2025Review
- Clinical implications of RAB13 expression in pan-cancer based on multi-databases integrative analysis.Scientific reports · 2023Article
- Article
- GediNET for discovering gene associations across diseases using knowledge based machine learning approach.Scientific reports · 2022Article
- Deep Neural Network Classifier for Alzheimer's Disease.Journal of student research · 2022Article
- Bioinformatics and System Biology Approach to Reveal the Interaction Network and the Therapeutic Implications for Non-Small Cell Lung Cancer Patients With COVID-19.Frontiers in pharmacology · 2022Article
- Integrated analysis of the molecular mechanisms in idiopathic pulmonary fibrosis.International journal of medical sciences · 2021Article
- Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation.Computational and mathematical methods in medicine · 2021Article
- Bioinformatics in translational drug discovery.Bioscience reports · 2017Review
- Systems genetics in "-omics" era: current and future development.Theory in biosciences = Theorie in den Biowissenschaften · 2013Review
- Improving disease gene prioritization using the semantic similarity of Gene Ontology terms.Bioinformatics (Oxford, England) · 2010Article
- Genome-wide profiling of p63 DNA-binding sites identifies an element that regulates gene expression during limb development in the 7q21 SHFM1 locus.PLoS genetics · 2010Article
- Prediction of human disease genes by human-mouse conserved coexpression analysis.PLoS computational biology · 2008Article
- Phenotype ontologies for mouse and man: bridging the semantic gap.Disease models & mechanismsReview
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the explosion in genomic and functional genomics information, methods for disease gene identification are rapidly evolving. Databases are now essential to the process of selecting candidate disease genes. Combining positional information with disease characteristics and functional information is the usual strategy by which candidate disease genes are selected. Enrichment for candidate disease genes, however, depends on the skills of the operating researcher. Over the past few years, a number of bioinformatics methods that enrich for the most likely candidate disease genes have been developed. Such in silico prioritisation methods may further improve by completion of datasets, by development of standardised ontologies across databases and species and, ultimately, by the integration of different strategies.
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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.