Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso
The research conducted undersampling and gene selection as a starting point for cancer classification in gene expression datasets with a high-dimensional and imbalanced class. It investigated whether implementing undersampling before gene selection gave better results than without implementing under...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Bina Nusantara University
2020-12-01
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| Series: | ComTech |
| Subjects: | |
| Online Access: | https://journal.binus.ac.id/index.php/comtech/article/view/6452 |
| _version_ | 1797717522830589952 |
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| author | Masithoh Yessi Rochayani Umu Sa'adah Ani Budi Astuti |
| author_facet | Masithoh Yessi Rochayani Umu Sa'adah Ani Budi Astuti |
| author_sort | Masithoh Yessi Rochayani |
| collection | DOAJ |
| description | The research conducted undersampling and gene selection as a starting point for cancer classification in gene expression datasets with a high-dimensional and imbalanced class. It investigated whether implementing undersampling before gene selection gave better results than without implementing undersampling. The used undersampling method was Random Undersampling (RUS), and for gene selection, it was Lasso. Then, the selected genes based on theory were validated. To explore the effectiveness of applying RUS before gene selection, the researchers used two gene expression datasets. Both of the datasets consisted of two classes, 1.545 observations and 10.935 genes, but had a different imbalance ratio. The results show that the proposed gene selection methods, namely Lasso and RUS + Lasso, can produce several important biomarkers, and the obtained model has high accuracy. However, the model is complicated since it involves too many genes. It also finds that undersampling is not affected when it is implemented in a less imbalanced class. Meanwhile, when the dataset is highly imbalanced, undersampling can remove a lot of information from the majority class. Nevertheless, the effectiveness of undersampling remains unclear. Simulation studies can be carried out in the next research to investigate when undersampling should be implemented. |
| first_indexed | 2024-03-12T08:37:36Z |
| format | Article |
| id | doaj.art-458ff97101bb4418a5895dd03de8dc94 |
| institution | Directory Open Access Journal |
| issn | 2087-1244 2476-907X |
| language | English |
| last_indexed | 2024-03-12T08:37:36Z |
| publishDate | 2020-12-01 |
| publisher | Bina Nusantara University |
| record_format | Article |
| series | ComTech |
| spelling | doaj.art-458ff97101bb4418a5895dd03de8dc942023-09-02T17:07:36ZengBina Nusantara UniversityComTech2087-12442476-907X2020-12-01112758110.21512/comtech.v11i2.64525570Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and LassoMasithoh Yessi Rochayani0Umu Sa'adah1Ani Budi Astuti2Universitas BrawijayaUniversitas BrawijayaUniversitas BrawijayaThe research conducted undersampling and gene selection as a starting point for cancer classification in gene expression datasets with a high-dimensional and imbalanced class. It investigated whether implementing undersampling before gene selection gave better results than without implementing undersampling. The used undersampling method was Random Undersampling (RUS), and for gene selection, it was Lasso. Then, the selected genes based on theory were validated. To explore the effectiveness of applying RUS before gene selection, the researchers used two gene expression datasets. Both of the datasets consisted of two classes, 1.545 observations and 10.935 genes, but had a different imbalance ratio. The results show that the proposed gene selection methods, namely Lasso and RUS + Lasso, can produce several important biomarkers, and the obtained model has high accuracy. However, the model is complicated since it involves too many genes. It also finds that undersampling is not affected when it is implemented in a less imbalanced class. Meanwhile, when the dataset is highly imbalanced, undersampling can remove a lot of information from the majority class. Nevertheless, the effectiveness of undersampling remains unclear. Simulation studies can be carried out in the next research to investigate when undersampling should be implemented.https://journal.binus.ac.id/index.php/comtech/article/view/6452biomarkershigh-dimensional imbalanced datasetrandom undersampling (rus)lasso hybrid method |
| spellingShingle | Masithoh Yessi Rochayani Umu Sa'adah Ani Budi Astuti Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso ComTech biomarkers high-dimensional imbalanced dataset random undersampling (rus) lasso hybrid method |
| title | Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso |
| title_full | Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso |
| title_fullStr | Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso |
| title_full_unstemmed | Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso |
| title_short | Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso |
| title_sort | finding biomarkers from a high dimensional imbalanced dataset using the hybrid method of random undersampling and lasso |
| topic | biomarkers high-dimensional imbalanced dataset random undersampling (rus) lasso hybrid method |
| url | https://journal.binus.ac.id/index.php/comtech/article/view/6452 |
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