ArticleBiometrics2011
Learning oncogenic pathways from binary genomic instability data.
Article in Biometrics, 2011. 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.
- Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap.Journal of genetics and genomics = Yi chuan xue bao · 2018Article
- A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity.Bioinformatics (Oxford, England) · 2018Article
- A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- A sparse Ising model with covariates.Biometrics · 2014Article
- Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Genomic instability, the propensity of aberrations in chromosomes, plays a critical role in the development of many diseases. High throughput genotyping experiments have been performed to study genomic instability in diseases. The output of such experiments can be summarized as high-dimensional binary vectors, where each binary variable records aberration status at one marker locus. It is of keen interest to understand how aberrations may interact with each other, as it provides insight into the process of the disease development. In this article, we propose a novel method, LogitNet, to infer such interactions among these aberration events. The method is based on penalized logistic regression with an extension to account for spatial correlation in the genomic instability data. We conduct extensive simulation studies and show that the proposed method performs well in the situations considered. Finally, we illustrate the method using genomic instability data from breast cancer samples.
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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.