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Accurate and reliable cancer classification based on probabilistic inference of pathway activity.
Su, Junjie; Yoon, Byung-Jun; Dougherty, Edward R.
Afiliação
  • Su J; Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America.
PLoS One ; 4(12): e8161, 2009 Dec 07.
Article em En | MEDLINE | ID: mdl-19997592
ABSTRACT
With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transdução de Sinais / Modelos Estatísticos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2009 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transdução de Sinais / Modelos Estatísticos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2009 Tipo de documento: Article País de afiliação: Estados Unidos
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