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

ABSTRACT: 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 and batch effects limit the applicability of traditional statistics and machine learning analysis. Here, we present a meta-transfer learning approach to transfer knowledge from big data and reduce the search space in data with small sample sizes. Few-shot learning algorithms integrate meta-learning to overcome data scarcity and data heterogeneity by transferring molecular pattern recognition models from datasets of unrelated domains. We explore few-shot learning models with large scale public data

SUBMITTER: Park Y 

PROVIDER: S-EPMC8598306 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature