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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...

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Autores principales: Ren, Xianwen, Wang, Yong, Chen, Luonan, Zhang, Xiang-Sun, Jin, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
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.
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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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