Models & Researchsingle celldeep learninggene setsbioinformatics
SDAN uses gene-set supervised deep learning for cell classification
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6.1
SDAN is a computational method that summarizes scRNA-seq differential expression results at the level of gene sets. These gene sets are learned via supervised deep learning with gene functional annotation for cell classification.
Scoring Rationale
A new supervised deep-learning approach that learns gene-set summaries from `scRNA-seq` differential expression, offering moderate relevance for single-cell classification and interpretability workflows.
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