ArticleGenome medicine2009
Linking genes to diseases: it's all in the data.
Article in Genome medicine, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Schizophrenia and Rheumatoid Arthritis Genetic Scenery: Potential Non-HLA Genes Involved in Both Diseases Relationship.The Yale journal of biology and medicine · 2024Article
- Network Analysis of Microarray Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- The Key Genes of Chronic Pancreatitis which Bridge Chronic Pancreatitis and Pancreatic Cancer Can be Therapeutic Targets.Pathology oncology research : POR · 2018Article
- brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets.Genome medicine · 2017Article
- Phenome-driven disease genetics prediction toward drug discovery.Bioinformatics (Oxford, England) · 2015Article
- Comparative analysis of a novel disease phenotype network based on clinical manifestations.Journal of biomedical informatics · 2015Article
- Quo vadis computational analysis of PPI data or why the future isn't here yet.Frontiers in genetics · 2015Article
- Identification of highly related references about gene-disease association.BMC bioinformatics · 2014Article
- An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.Artificial intelligence in medicine · 2014Article
- Mining cancer-specific disease comorbidities from a large observational health database.Cancer informatics · 2014Article
- Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature.Bioinformatics (Oxford, England) · 2013Article
- Mining breast cancer genes with a network based noise-tolerant approach.BMC systems biology · 2013Article
- Filtering for compound heterozygous sequence variants in non-consanguineous pedigrees.PloS one · 2013Article
- Computational tools for prioritizing candidate genes: boosting disease gene discovery.Nature reviews. Genetics · 2012Review
- Disease gene identification strategies for exome sequencing.European journal of human genetics : EJHG · 2012Review
- Mining the literature: new methods to exploit keyword profiles.Genome medicine · 2012Article
- Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.PLoS computational biology · 2012Article
- Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores.PloS one · 2012Article
- Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles.Genome medicine · 2012Article
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
Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches.
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.