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openalexFigshare2026-07-24Cited by 0

benchmark_DL_CNN

Guoji Guo

<i>Deep learning (DL) methods show promising potential for single-cell data analysis, yet required tremendous efforts in building the models. </i><i>To streamline the application of sequence-based DL methods in single-cell genomics, we established a two-layer CNN model as a baseline model and systematically evaluate how data characteristics, hyperparameter optimization, and advanced model architectures affect performance in sequence-to-expression and sequence-to-regulation tasks. We further explored the application of multi-task learning (MTL) frameworks for modeling cellular heterogeneity, evaluating the effectiveness of task grouping and balancing strategies, with particular focus on the prediction of rare cell types. </i><i>Our comprehensive benchmark efforts provide an actionable framework and valuable insights for guiding future research endeavors and facilitating the development of the sequence-based DL models capable of superior predictive performance in single-cell genomics.</i>

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openalexFigshare2026-07-24

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openalexFigshare2026-07-24

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openalexFigshare2026-07-23

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openalexFigshare2026-07-24

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