Transfer learning compensates limited data, batch effects and technological heterogeneity in single-cell sequencing

Tremendous advances in next-generation sequencing technology have enabled the accumulation of large amounts of omics data in various research areas over the past decade. However, study limitations due to small sample sizes, especially in rare disease clinical research, technological heterogeneity an...

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Bibliographic Details
Main Authors:	Hauschild, A.-C (Author), Heider, D. (Author), Park, Y. (Author)
Format:	Article
Language:	English
Published:	Oxford University Press 2021
Subjects:	Article autoencoder automated pattern recognition back propagation big data bioinformatics classification algorithm comparative study data processing deep neural network dimensionality reduction few-shot learning gene expression gene expression profiling genomics human human tissue k means clustering learning algorithm machine learning meta-transfer learning principal component analysis sample size single cell RNA seq transcriptome transcriptomics transfer of learning
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View Fulltext in Publisher

Transfer learning compensates limited data, batch effects and technological heterogeneity in single-cell sequencing

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