Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles
The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/12/1744 |
| _version_ | 1797459324174336000 |
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| author | Ania Mesa-Rodríguez Augusto Gonzalez Ernesto Estevez-Rams Pedro A. Valdes-Sosa |
| author_facet | Ania Mesa-Rodríguez Augusto Gonzalez Ernesto Estevez-Rams Pedro A. Valdes-Sosa |
| author_sort | Ania Mesa-Rodríguez |
| collection | DOAJ |
| description | The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity–entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results. |
| first_indexed | 2024-03-09T16:49:48Z |
| format | Article |
| id | doaj.art-56092bfb7bd442d58747c4f45e20fa4c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T16:49:48Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-56092bfb7bd442d58747c4f45e20fa4c2023-11-24T14:42:14ZengMDPI AGEntropy1099-43002022-11-012412174410.3390/e24121744Cancer Segmentation by Entropic Analysis of Ordered Gene Expression ProfilesAnia Mesa-Rodríguez0Augusto Gonzalez1Ernesto Estevez-Rams2Pedro A. Valdes-Sosa3The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Sciences and Technology of China, Chengdu 610054, ChinaThe Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Sciences and Technology of China, Chengdu 610054, ChinaFacultad de Física, Instituto de Ciencias y Tecnología de Materiales (IMRE), Universidad de La Habana, San Lazaro y L, La Habana 10400, CubaThe Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Sciences and Technology of China, Chengdu 610054, ChinaThe availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity–entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.https://www.mdpi.com/1099-4300/24/12/1744tumor discriminationgene expressionShannon entropyinformation distance |
| spellingShingle | Ania Mesa-Rodríguez Augusto Gonzalez Ernesto Estevez-Rams Pedro A. Valdes-Sosa Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles Entropy tumor discrimination gene expression Shannon entropy information distance |
| title | Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles |
| title_full | Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles |
| title_fullStr | Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles |
| title_full_unstemmed | Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles |
| title_short | Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles |
| title_sort | cancer segmentation by entropic analysis of ordered gene expression profiles |
| topic | tumor discrimination gene expression Shannon entropy information distance |
| url | https://www.mdpi.com/1099-4300/24/12/1744 |
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