GSEnet: feature extraction of gene expression data and its application to Leukemia classification
Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-03-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022228?viewType=HTML |
| _version_ | 1819156585978527744 |
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| author | Kun Yu Mingxu Huang Shuaizheng Chen Chaolu Feng Wei Li |
| author_facet | Kun Yu Mingxu Huang Shuaizheng Chen Chaolu Feng Wei Li |
| author_sort | Kun Yu |
| collection | DOAJ |
| description | Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy. |
| first_indexed | 2024-12-22T15:55:13Z |
| format | Article |
| id | doaj.art-1f7f8024f6a04ae1ac39f98ec5eb5285 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-22T15:55:13Z |
| publishDate | 2022-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-1f7f8024f6a04ae1ac39f98ec5eb52852022-12-21T18:20:48ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-03-011954881489110.3934/mbe.2022228GSEnet: feature extraction of gene expression data and its application to Leukemia classificationKun Yu0Mingxu Huang1Shuaizheng Chen2Chaolu Feng 3Wei Li 41. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110819, China 2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, Liaoning 110819, China3. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China3. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, Liaoning 110819, China 3. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, Liaoning 110819, China 3. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, ChinaGene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy.https://www.aimspress.com/article/doi/10.3934/mbe.2022228?viewType=HTMLdeep learninggene expressionfeature extractionresnetsenet |
| spellingShingle | Kun Yu Mingxu Huang Shuaizheng Chen Chaolu Feng Wei Li GSEnet: feature extraction of gene expression data and its application to Leukemia classification Mathematical Biosciences and Engineering deep learning gene expression feature extraction resnet senet |
| title | GSEnet: feature extraction of gene expression data and its application to Leukemia classification |
| title_full | GSEnet: feature extraction of gene expression data and its application to Leukemia classification |
| title_fullStr | GSEnet: feature extraction of gene expression data and its application to Leukemia classification |
| title_full_unstemmed | GSEnet: feature extraction of gene expression data and its application to Leukemia classification |
| title_short | GSEnet: feature extraction of gene expression data and its application to Leukemia classification |
| title_sort | gsenet feature extraction of gene expression data and its application to leukemia classification |
| topic | deep learning gene expression feature extraction resnet senet |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022228?viewType=HTML |
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