ArticleBiometrics2009
A latent class model with hidden Markov dependence for array CGH data.
Article in Biometrics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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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Who cites it
3 citing papers in PubMed.
- Generalized species sampling priors with latent Beta reinforcements.Journal of the American Statistical Association · 2014Article
- Detection of candidate tumor driver genes using a fully integrated Bayesian approach.Statistics in medicine · 2014Article
- Hidden Markov models for zero-inflated Poisson counts with an application to substance use.Statistics in medicine · 2011Article
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Authors and funding
6 authors.
Funding
Abstract
Array CGH is a high-throughput technique designed to detect genomic alterations linked to the development and progression of cancer. The technique yields fluorescence ratios that characterize DNA copy number change in tumor versus healthy cells. Classification of tumors based on aCGH profiles is of scientific interest but the analysis of these data is complicated by the large number of highly correlated measures. In this article, we develop a supervised Bayesian latent class approach for classification that relies on a hidden Markov model to account for the dependence in the intensity ratios. Supervision means that classification is guided by a clinical endpoint. Posterior inferences are made about class-specific copy number gains and losses. We demonstrate our technique on a study of brain tumors, for which our approach is capable of identifying subsets of tumors with different genomic profiles, and differentiates classes by survival much better than unsupervised methods.
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Registered trials
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