A new dimensionality reduction technique based on the Wavelet Transform for cancer classification
Abstract Problem DNA methylation and hydroxymethylation have become important epigenetic markers for early detection of cancer. In recent years, there has been a significant increase in both the number of research works on this topic and the number and size of labeled databases with some type of can...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01039-9 |
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| author | Lisardo Fernández Mariano Pérez Juan M. Orduña José M. Alcaraz |
| author_facet | Lisardo Fernández Mariano Pérez Juan M. Orduña José M. Alcaraz |
| author_sort | Lisardo Fernández |
| collection | DOAJ |
| description | Abstract Problem DNA methylation and hydroxymethylation have become important epigenetic markers for early detection of cancer. In recent years, there has been a significant increase in both the number of research works on this topic and the number and size of labeled databases with some type of cancer. Although the advent of methylation microarrays such as the HumanMethylation450 platform has greatly reduced the dimensionality of the problem from billions to 450K positions, this data size is still too large to be processed by machine learning algorithms for cancer prediction and classification. Aim In the particular case of methylation, an efficient dimensionality reduction technique should also preserve the spatial information of the original data in order to properly predict and classify cancer. Method This work proposes a new approach for data dimensionality reduction technique based on the Discrete Wavelet Transform (DWT), which preserves spatial information. We have evaluated the proposed technique with a dataset collected from the most important cancer databases according to their social impact, and we have compared our proposal to five well-known dimensionality reduction techniques: PCA, ReliefF, Isomap, LLE and UMAP. Results The performance evaluation results show that the proposed technique significantly reduces both the computational resources and the execution time required for dimensionality reduction. In addition, it significantly improves the accuracy achieved in the classification by a support vector machine when it uses as input data the resulting dataset yielded by each technique. Conclusions The proposed approach based on the DWT can be considered as an efficient alternative for those cases where dimensionality reduction must preserve spatial information. |
| format | Article |
| id | doaj-art-92ffa877cd6d4ef88f6d621818968819 |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-92ffa877cd6d4ef88f6d6218189688192025-01-26T12:37:42ZengSpringerOpenJournal of Big Data2196-11152025-01-0112112310.1186/s40537-024-01039-9A new dimensionality reduction technique based on the Wavelet Transform for cancer classificationLisardo Fernández0Mariano Pérez1Juan M. Orduña2José M. Alcaraz3Departamento de Informática, Universidad de ValenciaDepartamento de Informática, Universidad de ValenciaDepartamento de Informática, Universidad de ValenciaSchool of Computing, Engineering and Physical Sciences, University of the West of ScotlandAbstract Problem DNA methylation and hydroxymethylation have become important epigenetic markers for early detection of cancer. In recent years, there has been a significant increase in both the number of research works on this topic and the number and size of labeled databases with some type of cancer. Although the advent of methylation microarrays such as the HumanMethylation450 platform has greatly reduced the dimensionality of the problem from billions to 450K positions, this data size is still too large to be processed by machine learning algorithms for cancer prediction and classification. Aim In the particular case of methylation, an efficient dimensionality reduction technique should also preserve the spatial information of the original data in order to properly predict and classify cancer. Method This work proposes a new approach for data dimensionality reduction technique based on the Discrete Wavelet Transform (DWT), which preserves spatial information. We have evaluated the proposed technique with a dataset collected from the most important cancer databases according to their social impact, and we have compared our proposal to five well-known dimensionality reduction techniques: PCA, ReliefF, Isomap, LLE and UMAP. Results The performance evaluation results show that the proposed technique significantly reduces both the computational resources and the execution time required for dimensionality reduction. In addition, it significantly improves the accuracy achieved in the classification by a support vector machine when it uses as input data the resulting dataset yielded by each technique. Conclusions The proposed approach based on the DWT can be considered as an efficient alternative for those cases where dimensionality reduction must preserve spatial information.https://doi.org/10.1186/s40537-024-01039-9Dimensionality reductionCancer classificationDNA methylation analysisWavelet TransformMachine learning classification |
| spellingShingle | Lisardo Fernández Mariano Pérez Juan M. Orduña José M. Alcaraz A new dimensionality reduction technique based on the Wavelet Transform for cancer classification Journal of Big Data Dimensionality reduction Cancer classification DNA methylation analysis Wavelet Transform Machine learning classification |
| title | A new dimensionality reduction technique based on the Wavelet Transform for cancer classification |
| title_full | A new dimensionality reduction technique based on the Wavelet Transform for cancer classification |
| title_fullStr | A new dimensionality reduction technique based on the Wavelet Transform for cancer classification |
| title_full_unstemmed | A new dimensionality reduction technique based on the Wavelet Transform for cancer classification |
| title_short | A new dimensionality reduction technique based on the Wavelet Transform for cancer classification |
| title_sort | new dimensionality reduction technique based on the wavelet transform for cancer classification |
| topic | Dimensionality reduction Cancer classification DNA methylation analysis Wavelet Transform Machine learning classification |
| url | https://doi.org/10.1186/s40537-024-01039-9 |
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