ArticleBMC bioinformatics2005
Identification of significant periodic genes in microarray gene expression data.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Nonlinear-model-based analysis methods for time-course gene expression data.TheScientificWorldJournal · 2014Article
- Nonlinear model-based method for clustering periodically expressed genes.TheScientificWorldJournal · 2011Article
- Article
- Gene expression model (in)validation by Fourier analysis.BMC systems biology · 2010Article
- Robust discovery of periodically expressed genes using the laplace periodogram.BMC bioinformatics · 2009Article
- Cyclebase.org--a comprehensive multi-organism online database of cell-cycle experiments.Nucleic acids research · 2008Article
- Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data.BMC bioinformatics · 2007Article
- Transcriptome changes and cAMP oscillations in an archaeal cell cycle.BMC cell biology · 2007Article
- Spectral estimation in unevenly sampled space of periodically expressed microarray time series data.BMC bioinformatics · 2007Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne frequent application of microarray experiments is in the study of monitoring gene activities in a cell during cell cycle or cell division. A new challenge for analyzing the microarray experiments is to identify genes that are statistically significantly periodically expressed during the cell cycle. Such a challenge occurs due to the large number of genes that are simultaneously measured, a moderate to small number of measurements per gene taken at different time points, and high levels of non-normal random noises inherited in the data.
resultsBased on two statistical hypothesis testing methods for identifying periodic time series, a novel statistical inference approach, the C&G procedure, is proposed to effectively screen out statistically significantly periodically expressed genes. The approach is then applied to yeast and bacterial cell cycle gene expression data sets, as well as to human fibroblasts and human cancer cell line data sets, and significantly periodically expressed genes are successfully identified.
conclusionThe C&G procedure proposed is an effective method for identifying statistically significant periodic genes in microarray time series gene expression data.
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.