ReviewMolecular genetics and genomics : MGG2012
Candidate gene prioritization.
Review in Molecular genetics and genomics : MGG, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 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
7 citing papers in PubMed.
- In Silico Analysis of the Effect ofMedicina (Kaunas, Lithuania) · 2023Article
- Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network.PLoS computational biology · 2017Article
- An integrated network of Arabidopsis growth regulators and its use for gene prioritization.Scientific reports · 2015Article
- Microarray enriched gene rank.BioData mining · 2015Article
- A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms.BMC bioinformatics · 2014Article
- Identification of the causative gene for Simmental arachnomelia syndrome using a network-based disease gene prioritization approach.PloS one · 2013Article
- Profiling, Bioinformatic, and Functional Data on the Developing Olfactory/GnRH System Reveal Cellular and Molecular Pathways Essential for This Process and Potentially Relevant for the Kallmann Syndrome.Frontiers in endocrinology · 2013Article
Corrections and comments
- Retraction · 2015-09-14Concerns/Issues about Image · Copyright Claims · Concerns/Issues about Referencing/Attributions ·
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Candidate gene identification is typically labour intensive, involving laboratory experiments required to corroborate or disprove any hypothesis for a nominated candidate gene being considered the causative gene. The traditional approach to reduce the number of candidate genes entails fine-mapping studies using markers and pedigrees. Gene prioritization establishes the ranking of candidate genes based on their relevance to the biological process of interest, from which the most promising genes can be selected for further analysis. To date, many computational methods have focused on the prediction of candidate genes by analysis of their inherent sequence characteristics and similarity with respect to known disease genes, as well as their functional annotation. In the last decade, several computational tools for prioritizing candidate genes have been proposed. A large number of them are web-based tools, while others are standalone applications that install and run locally. This review attempts to take a close look at gene prioritization criteria, as well as candidate gene prioritization algorithms, and thus provide a comprehensive synopsis of the subject matter.
Indexed as
Identifiers
22893106What 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.