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ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions
Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classificat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3575836/ https://www.ncbi.nlm.nih.gov/pubmed/23262226 http://dx.doi.org/10.1093/nar/gks1288 |
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author | Ren, Xianwen Wang, Yong Chen, Luonan Zhang, Xiang-Sun Jin, Qi |
author_facet | Ren, Xianwen Wang, Yong Chen, Luonan Zhang, Xiang-Sun Jin, Qi |
author_sort | Ren, Xianwen |
collection | PubMed |
description | Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN. |
format | Online Article Text |
id | pubmed-3575836 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-35758362013-02-19 ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions Ren, Xianwen Wang, Yong Chen, Luonan Zhang, Xiang-Sun Jin, Qi Nucleic Acids Res Methods Online Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN. Oxford University Press 2013-02 2012-12-21 /pmc/articles/PMC3575836/ /pubmed/23262226 http://dx.doi.org/10.1093/nar/gks1288 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
spellingShingle | Methods Online Ren, Xianwen Wang, Yong Chen, Luonan Zhang, Xiang-Sun Jin, Qi ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
title | ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
title_full | ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
title_fullStr | ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
title_full_unstemmed | ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
title_short | ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
title_sort | ellipsoidfn: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3575836/ https://www.ncbi.nlm.nih.gov/pubmed/23262226 http://dx.doi.org/10.1093/nar/gks1288 |
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