Incorporating hierarchical information into multiple instance learning for patient phenotype prediction with single-cell RNA-sequencing data.

Abstract excerpt

MOTIVATION: Multiple instance learning (MIL) provides a structured approach to patient phenotype prediction with single-cell RNA-sequencing (scRNA-seq) data. However, existing MIL methods tend to overlook the hierarchical structure inherent in scRNA-seq data, especially the biological groupings of cells or cell types. This limitation may lead to suboptimal performance and poor interpretability at higher levels of...

Topics

• Single-Cell Analysis
• Humans
• Phenotype
• Sequence Analysis, RNA
• Machine Learning
• Computational Biology