ArticleOncogene2002
Mathematical modeling of noise and discovery of genetic expression classes in gliomas.
Article in Oncogene, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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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.
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Who cites it
5 citing papers in PubMed, 25 citations in OpenAlex.
- Epigenetic silencing of miR-342-3p in B cell lymphoma and its impact on autophagy.Clinical epigenetics · 2020Article
- PPARG Drives Molecular Networks as an Inhibitor for the Pathologic Development and Progression of Lung Adenocarcinoma.PPAR research · 2020Article
- Upregulation of ASCL1 and inhibition of Notch signaling pathway characterize progressive astrocytoma.Oncogene · 2005Article
- Article
- Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.Nucleic acids research · 2004Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
Abstract
The microarray array experimental system generates noisy data that require validation by other experimental methods for measuring gene expression. Here we present an algebraic modeling of noise that extracts expression measurements true to a high degree of confidence. This work profiles the expression of 19 200 cDNAs in 35 human gliomas; the experiments are designed to generate four replicate spots/gene with switching of probes. The validity of the extracted measurements is confirmed by: (1) cluster analysis that generates a molecular classification differentiating glioblastoma from lower-grade tumors and radiation necrosis; (2) By what other investigators have reported in gliomas using paradigms for assaying molecular expression other than gene profiling; and (3) Real-time RT-PCR. The results yield a genetic analysis of gliomas and identify classes of genetic expression that link novel genes to the biology of gliomas.
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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.