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As a pervasive issue, missing data may influence the data modeling performance and lead to more difficulties of completing the desired tasks. Many approaches have been developed for missing data imputation. Recently, by taking advantage of the emerging generative adversarial network (GAN), an effective missing data imputation approach termed generative adversarial imputation nets (GAIN) was developed. However, its modeling architecture may still lead to significant imputation bias. In addition, with the GAN structure, the training process of GAIN may be unstable and the imputation variation may be high. Hence, to address these two limitations, the ensemble GAIN with selective multi-generator (ESM-GAIN) is proposed to improve the imputation accuracy and robustness. The contributions of the proposed ESM-GAIN consist of two aspects: (1) a selective multi-generation framework is proposed to identify high-quality imputations; (2) an ensemble learning framework is incorporated for GAIN imputation to improve the imputation robustness. The effectiveness of the proposed ESM-GAIN is validated by both numerical simulation and two real-world breast cancer datasets.
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DeepMicroGen: a generative adversarial network-based method for longitudinal microbiome data imputation
Abstract MotivationThe human microbiome, which is linked to various diseases by growing evidence, has a profound impact on human health. Since changes in the composition of the microbiome across time are associated with disease and clinical outcomes, microbiome analysis should be performed in a longitudinal study. However, due to limited sample sizes and differing numbers of timepoints for different subjects, a significant amount of data cannot be utilized, directly affecting the quality of analysis results. Deep generative models have been proposed to address this lack of data issue. Specifically, a generative adversarial network (GAN) has been successfully utilized for data augmentation to improve prediction tasks. Recent studies have also shown improved performance of GAN-based models for missing value imputation in a multivariate time series dataset compared with traditional imputation methods. ResultsThis work proposes DeepMicroGen, a bidirectional recurrent neural network-based GAN model, trained on the temporal relationship between the observations, to impute the missing microbiome samples in longitudinal studies. DeepMicroGen outperforms standard baseline imputation methods, showing the lowest mean absolute error for both simulated and real datasets. Finally, the proposed model improved the predicted clinical outcome for allergies, by providing imputation for an incomplete longitudinal dataset used to train the classifier. Availability and implementationDeepMicroGen is publicly available at https://github.com/joungmin-choi/DeepMicroGen.
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- Award ID(s):
- 2004751
- PAR ID:
- 10414420
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 39
- Issue:
- 5
- ISSN:
- 1367-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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