ArticleBioinformatics (Oxford, England)2008
A probe-density-based analysis method for array CGH data: simulation, normalization and centralization.
Article in Bioinformatics (Oxford, England), 2008. 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, 24 citations in OpenAlex.
- An Integrated Approach for RNA-seq Data Normalization.Cancer informatics · 2016Article
- Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology.Annals of oncology : official journal of the European Society for Medical Oncology · 2015Article
- Identifying Human Genome-Wide CNV, LOH and UPD by Targeted Sequencing of Selected Regions.PloS one · 2014Article
- PAIR: paired allelic log-intensity-ratio-based normalization method for SNP-CGH arrays.Bioinformatics (Oxford, England) · 2013Article
- The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation.Frontiers in genetics · 2013Article
- Article
- Genomic diversity of Lactobacillus salivarius.Applied and environmental microbiology · 2011Article
- A method for detecting significant genomic regions associated with oral squamous cell carcinoma using aCGH.Medical & biological engineering & computing · 2010Article
- CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations.BMC genomics · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
motivationGenomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results.
resultsWe demonstrated a novel aCGH normalization algorithm, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a hidden Markov model (HMM) to simulate a series of aCGH experiments with random DNA copy number alterations that are used to validate the performance of our normalization. In addition, we applied the proposed normalization algorithm to an aCGH study of lung cancer cell lines. By using the proposed algorithm, data quality and the reliability of experimental results are significantly improved, and the distinct patterns of DNA copy number alternations are observed among those lung cancer cell lines. SUPPLEMENTARY INFORMATION: Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary.
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.