ReviewTrends in genetics : TIG2006
Inferential literacy for experimental high-throughput biology.
Review in Trends in genetics : TIG, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 34 citations in OpenAlex.
- mRNA expression analysis of the hippocampus in a vervet monkey model of fetal alcohol spectrum disorder.Journal of neurodevelopmental disorders · 2022Article
- Cyber-T web server: differential analysis of high-throughput data.Nucleic acids research · 2012Article
- Refinement of the genetics of the host response to Salmonella infection in MOLF/Ei: regulation of type 1 IFN and TRP3 pathways by Ity2.Genes and immunity · 2012Article
- MIPHENO: data normalization for high throughput metabolite analysis.BMC bioinformatics · 2012Article
- A white-box approach to microarray probe response characterization: the BaFL pipeline.BMC bioinformatics · 2009Article
- EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.BMC bioinformatics · 2009Article
- Repeatability of published microarray gene expression analyses.Nature genetics · 2009Article
- Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification.BMC bioinformatics · 2008Article
- Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?Malaria journal · 2006Review
- The end of the microarray Tower of Babel: will universal standards lead the way?Journal of biomolecular techniques : JBT · 2006Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many biologists believe that data analysis expertise lags behind the capacity for producing high-throughput data. One view within the bioinformatics community is that biological scientists need to develop algorithmic skills to meet the demands of the new technologies. In this article, we argue that the broader concept of inferential literacy, which includes understanding of data characteristics, experimental design and statistical analysis, in addition to computation, more adequately encompasses what is needed for efficient progress in high-throughput biology.
Indexed as
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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.