ArticleStatistics and its interface2009
Identification of cancer-associated gene clusters and genes via clustering penalization.
Article in Statistics and its interface, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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.
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Who cites it
6 citing papers in PubMed.
- Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm.BioMed research international · 2016Article
- Identification of Genes Discriminating Multiple Sclerosis Patients from Controls by Adapting a Pathway Analysis Method.PloS one · 2016Article
- Ranking prognosis markers in cancer genomic studies.Briefings in bioinformatics · 2011Article
- Semiparametric prognosis models in genomic studies.Briefings in bioinformatics · 2010Article
- Identification of non-Hodgkin's lymphoma prognosis signatures using the CTGDR method.Bioinformatics (Oxford, England) · 2010Article
- Detection of gene pathways with predictive power for breast cancer prognosis.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Identification of genes associated with cancer development and progression using microarray data is challenging because of the high dimensionality and cluster structure of gene expressions. Here the clusters are composed of multiple genes with coordinated biological functions and/or correlated expressions. In this article, we first propose a hybrid approach for clustering gene expressions. The hybrid approach uses both pathological pathway information and correlations of gene expressions. We propose using the group bridge, a novel clustering penalization approach, for analysis of cancer microarray data. The group bridge approach explicitly accounts for the cluster structure of gene expressions, and is capable of selecting gene clusters and genes within those selected clusters that are associated with cancer. We also develop an iterative algorithm for computing the group bridge estimator. Analysis of three cancer microarray datasets shows that the proposed approach can identify biologically meaningful gene clusters and genes within those identified clusters.
Identifiers
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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.