Deep Learning Methods for Omics Data Imputation
One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However,...
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MDPI AG
2023-10-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/12/10/1313 |
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| author | Lei Huang Meng Song Hui Shen Huixiao Hong Ping Gong Hong-Wen Deng Chaoyang Zhang |
| author_facet | Lei Huang Meng Song Hui Shen Huixiao Hong Ping Gong Hong-Wen Deng Chaoyang Zhang |
| author_sort | Lei Huang |
| collection | DOAJ |
| description | One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field. |
| first_indexed | 2024-03-10T21:25:12Z |
| format | Article |
| id | doaj.art-208961ba39054211a6827c8ebe0f54d8 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-10T21:25:12Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-208961ba39054211a6827c8ebe0f54d82023-11-19T15:43:32ZengMDPI AGBiology2079-77372023-10-011210131310.3390/biology12101313Deep Learning Methods for Omics Data ImputationLei Huang0Meng Song1Hui Shen2Huixiao Hong3Ping Gong4Hong-Wen Deng5Chaoyang Zhang6School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USASchool of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USACenter for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70112, USADivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USAEnvironmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS 39180, USACenter for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70112, USASchool of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USAOne common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.https://www.mdpi.com/2079-7737/12/10/1313omics imputationdeep learningmulti-omics imputation |
| spellingShingle | Lei Huang Meng Song Hui Shen Huixiao Hong Ping Gong Hong-Wen Deng Chaoyang Zhang Deep Learning Methods for Omics Data Imputation Biology omics imputation deep learning multi-omics imputation |
| title | Deep Learning Methods for Omics Data Imputation |
| title_full | Deep Learning Methods for Omics Data Imputation |
| title_fullStr | Deep Learning Methods for Omics Data Imputation |
| title_full_unstemmed | Deep Learning Methods for Omics Data Imputation |
| title_short | Deep Learning Methods for Omics Data Imputation |
| title_sort | deep learning methods for omics data imputation |
| topic | omics imputation deep learning multi-omics imputation |
| url | https://www.mdpi.com/2079-7737/12/10/1313 |
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