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Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis
BACKGROUND: Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics ma...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3194236/ https://www.ncbi.nlm.nih.gov/pubmed/21989191 http://dx.doi.org/10.1186/1471-2164-12-S2-S5 |
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author | Wang, Zhenyu Palade, Vasile |
author_facet | Wang, Zhenyu Palade, Vasile |
author_sort | Wang, Zhenyu |
collection | PubMed |
description | BACKGROUND: Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules. However, the advantages offered by fuzzy-based techniques in microarray data analysis have not yet been fully explored in the literature. Although some recently developed fuzzy-based modeling approaches can provide satisfactory classification results, the rule bases generated by most of the reported fuzzy models for gene expression data are still too large to be easily comprehensible. RESULTS: In this paper, we develop some Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) methods for analysing high dimensional bio-medical data sets, such as microarray gene expression data and proteomics mass spectroscopy data. We mainly focus on evaluating our proposed models on microarray gene expression cancer data sets, i.e., the lung cancer data set and the colon cancer data set, but we extend our investigations to other type of cancer data set, such as the ovarian cancer data set. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, can be successfully obtained for challenging microarray gene expression datasets. CONCLUSIONS: We believe that fuzzy-based techniques, and in particular the methods proposed in this paper, can be very useful tools in dealing with high dimensional cancer data. We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored. |
format | Online Article Text |
id | pubmed-3194236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31942362011-10-17 Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis Wang, Zhenyu Palade, Vasile BMC Genomics Proceedings BACKGROUND: Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules. However, the advantages offered by fuzzy-based techniques in microarray data analysis have not yet been fully explored in the literature. Although some recently developed fuzzy-based modeling approaches can provide satisfactory classification results, the rule bases generated by most of the reported fuzzy models for gene expression data are still too large to be easily comprehensible. RESULTS: In this paper, we develop some Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) methods for analysing high dimensional bio-medical data sets, such as microarray gene expression data and proteomics mass spectroscopy data. We mainly focus on evaluating our proposed models on microarray gene expression cancer data sets, i.e., the lung cancer data set and the colon cancer data set, but we extend our investigations to other type of cancer data set, such as the ovarian cancer data set. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, can be successfully obtained for challenging microarray gene expression datasets. CONCLUSIONS: We believe that fuzzy-based techniques, and in particular the methods proposed in this paper, can be very useful tools in dealing with high dimensional cancer data. We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored. BioMed Central 2011-07-27 /pmc/articles/PMC3194236/ /pubmed/21989191 http://dx.doi.org/10.1186/1471-2164-12-S2-S5 Text en Copyright ©2011 Wang and Palade; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Wang, Zhenyu Palade, Vasile Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
title | Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
title_full | Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
title_fullStr | Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
title_full_unstemmed | Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
title_short | Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
title_sort | building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3194236/ https://www.ncbi.nlm.nih.gov/pubmed/21989191 http://dx.doi.org/10.1186/1471-2164-12-S2-S5 |
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