ArticleBest practice & research. Clinical haematology2009
Analysis of DNA microarray expression data.
Article in Best practice & research. Clinical haematology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Deep learning assisted cancer disease prediction from gene expression data using WT-GAN.BMC medical informatics and decision making · 2024Article
- Cross-platform validation of a mouse blood gene signature for quantitative reconstruction of radiation dose.Scientific reports · 2022Article
- Neutrophil chemotaxis and transcriptomics in term and preterm neonates.Translational research : the journal of laboratory and clinical medicine · 2017Article
- Unique transcriptomic response to sepsis is observed among patients of different age groups.PloS one · 2017Article
- Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.BioMed research international · 2016Article
- Radiation dose-rate effects on gene expression for human biodosimetry.BMC medical genomics · 2015Article
- mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.BioMed research international · 2015Article
- An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.TheScientificWorldJournal · 2014Article
- Quality assurance of RNA expression profiling in clinical laboratories.The Journal of molecular diagnostics : JMD · 2012Review
- Poly(ADP-ribose) polymerase inhibition enhances p53-dependent and -independent DNA damage responses induced by DNA damaging agent.Cell cycle (Georgetown, Tex.) · 2011Article
- Article
- Clinical implementation of RNA signatures for pharmacogenomic decision-making.Pharmacogenomics and personalized medicine · 2011Article
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
DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. Microarray based studies require clear objectives for selecting cases and appropriate analysis methods. Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed. Recent literature reviews indicate that serious problems of analysis exist a substantial proportion of publications. This manuscript attempts to provide a non-technical summary of the key principles of statistical design and analysis for studies that utilize microarray expression profiling.
Indexed as
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.