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

ABSTRACT:

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 cellular division.

Results

To address this gap, we present a novel approach to incorporate hierarchical information into the attention-based MIL framework. Specifically, our model applies the attention-based aggregation mechanism over both cells and cell types, thus enforcing a hierarchical structure on the flow of information throughout the model. Across extensive e

SUBMITTER: Do C 

PROVIDER: S-EPMC12261414 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature