Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/48221
Title: Deep learning methods for omics data imputation
Authors: Huang, Lei
Song, Meng
Shen, Hui
Hong, Huixiao
Gong, Ping
Deng, Hong-Wen
Zhang, Chaoyang
Keywords: Omics imputation
Deep learning
Multi-omics imputation
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/EL MP-24-3
Is Version Of: Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Hong-Wen Deng, and Chaoyang Zhang. "Deep learning methods for omics data imputation." Biology 12, no. 10 (2023): 1313. https://doi.org/10.3390/biology12101313
Abstract: 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 nonmonotonic 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.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/EL MP-24-3
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/48221
http://dx.doi.org/10.21079/11681/48221
Appears in Collections:Miscellaneous Paper

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