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A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main...

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Detalles Bibliográficos
Autores principales: Seok, Junhee, Davis, Ronald W., Xiao, Wenzhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4416884/
https://www.ncbi.nlm.nih.gov/pubmed/25933378
http://dx.doi.org/10.1371/journal.pone.0122103
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author Seok, Junhee
Davis, Ronald W.
Xiao, Wenzhong
author_facet Seok, Junhee
Davis, Ronald W.
Xiao, Wenzhong
author_sort Seok, Junhee
collection PubMed
description Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.
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spelling pubmed-44168842015-05-07 A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data Seok, Junhee Davis, Ronald W. Xiao, Wenzhong PLoS One Research Article Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge. Public Library of Science 2015-05-01 /pmc/articles/PMC4416884/ /pubmed/25933378 http://dx.doi.org/10.1371/journal.pone.0122103 Text en © 2015 Seok et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Seok, Junhee
Davis, Ronald W.
Xiao, Wenzhong
A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
title A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
title_full A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
title_fullStr A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
title_full_unstemmed A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
title_short A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
title_sort hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4416884/
https://www.ncbi.nlm.nih.gov/pubmed/25933378
http://dx.doi.org/10.1371/journal.pone.0122103
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