Data Augmentation Techniques to Improve Metabolomic Analysis in Niemann-Pick Type C Disease-Reference-Cited by-同舟云学术

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Data Augmentation Techniques to Improve Metabolomic Analysis in Niemann-Pick Type C Disease

Published:2022 Issue: Volume: Page:78-91
ISSN:0302-9743
Container-title:Computational Science – ICCS 2022
language:
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Author:

Moreno-Barea Francisco J.^ORCID,Franco Leonardo^ORCID,Elizondo David^ORCID,Grootveld Martin^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-08757-8_8

Reference26 articles.

1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/a:1010933404324

2. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953

3. Chong, J., et al.: MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucl. Acids Res. 46(W1), W486–W494 (2018). https://doi.org/10.1093/nar/gky310

4. Cougnoux, A., et al.: Necroptosis in Niemann-Pick disease, type C1: a potential therapeutic target. Cell Death Dis. 7(3), e2147–e2147 (2016). https://doi.org/10.1038/cddis.2016.16

5. Douzas, G., Bacao, F.: Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst. Appl. 91, 464–471 (2018). https://doi.org/10.1016/j.eswa.2017.09.030

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1. Data Augmentation Meta-Classifier Scheme for imbalanced data sets;2022 IEEE Symposium Series on Computational Intelligence (SSCI);2022-12-04

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