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Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer
BACKGROUND: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and dis...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448565/ https://www.ncbi.nlm.nih.gov/pubmed/25986937 http://dx.doi.org/10.1186/s12859-015-0565-5 |
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author | Sachnev, Vasily Saraswathi, Saras Niaz, Rashid Kloczkowski, Andrzej Suresh, Sundaram |
author_facet | Sachnev, Vasily Saraswathi, Saras Niaz, Rashid Kloczkowski, Andrzej Suresh, Sundaram |
author_sort | Sachnev, Vasily |
collection | PubMed |
description | BACKGROUND: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. RESULTS: BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50% of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. CONCLUSIONS: We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4% which is between 21.6% and 38% higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0565-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4448565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44485652015-05-30 Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer Sachnev, Vasily Saraswathi, Saras Niaz, Rashid Kloczkowski, Andrzej Suresh, Sundaram BMC Bioinformatics Research Article BACKGROUND: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. RESULTS: BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50% of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. CONCLUSIONS: We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4% which is between 21.6% and 38% higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0565-5) contains supplementary material, which is available to authorized users. BioMed Central 2015-05-20 /pmc/articles/PMC4448565/ /pubmed/25986937 http://dx.doi.org/10.1186/s12859-015-0565-5 Text en © Sachnev et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Sachnev, Vasily Saraswathi, Saras Niaz, Rashid Kloczkowski, Andrzej Suresh, Sundaram Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer |
title | Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer |
title_full | Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer |
title_fullStr | Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer |
title_full_unstemmed | Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer |
title_short | Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer |
title_sort | multi-class bcga-elm based classifier that identifies biomarkers associated with hallmarks of cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448565/ https://www.ncbi.nlm.nih.gov/pubmed/25986937 http://dx.doi.org/10.1186/s12859-015-0565-5 |
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