An Efficient and Reliable scRNA-seq Data Imputation Method Using Variational Autoencoders
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65522-7_8
Reference21 articles.
1. Amodio, M., et al.: Exploring single-cell data with deep multitasking neural networks. Nat. Methods 16(11), 1139–1145 (2019)
2. Communications in Computer and Information Science;A Anaissi,2023
3. Anaissi, A., Zandavi, S.M.: Multi-objective autoencoder for fault detection and diagnosis in higher-order data. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
4. Anaissi, A., Zandavi, S.M., Suleiman, B., Naji, M., Braytee, A.: Multi-objective variational autoencoder: an application for smart infrastructure maintenance. Appl. Intell. 53(10), 12047–12062 (2023)
5. Arisdakessian, C., Poirion, O., Yunits, B., Zhu, X., Garmire, L.X.: DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-Seq data. Genome Biol. 20(1), 1–14 (2019)
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