ReviewMolecular biotechnology2007
Microarray analysis: basic strategies for successful experiments.
Review in Molecular biotechnology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Whole Blood Gene Expression Profiling in Preclinical and Clinical Cattle Infected with Atypical Bovine Spongiform Encephalopathy.PloS one · 2016Article
- Conventional and molecular diagnostic testing for the acute neurologic patient.Journal of veterinary emergency and critical care (San Antonio, Tex. : 2001) · 2010Review
- Application of "omics" to prion biomarker discovery.Journal of biomedicine & biotechnology · 2010Review
- Innate immune modulation by RNA viruses: emerging insights from functional genomics.Nature reviews. Immunology · 2008Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Microarrays offer a powerful approach to the analysis of gene expression that can be used for a wide variety of experimental purposes. However, there are several types of microarray platforms that are available. In addition, microarray experiments are expensive and generate complicated data sets that can be difficult to interpret. Success with microarray approaches requires a sound experimental design and a coordinated and appropriate use of statistical tools. Here, the advantages and pitfalls of utilizing microarrays are discussed, as are practical strategies to help novice users succeed with this method that can empower them with the ability to assay changes in gene expression at the whole genome level.
Indexed as
Identifiers
17873407What 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.