Josephine Yates

GRASS-MIL predicts phenotypes from spatial single-cell data and decomposes predictions into specific cellular neighborhoods, showing improved performance over composition-based methods.

This study identifies a loss of osteoblastic lineage fidelity in metastatic osteosarcoma through single-nucleus RNA sequencing, linking it to disease progression and potential therapeutic targets.

This study evaluates agentic AI's ability to support biological discovery in autonomous and human-AI copilot settings using multi-omic cancer datasets.

UniversalEPI predicts cell type-specific and differential chromatin interactions using DNA sequence and ATAC-seq data, showing robust performance across cell types and conditions.

SpatialFusion identifies spatial niches through pathway activation patterns, revealing pre-malignant and malignant microenvironments in cancer tissues.

H&Enium aligns spatial transcriptomics and H&E imaging data using a contrastive framework to improve cell type classification and gene expression prediction.

The paper analyzes multiomic data from primary and metastatic esophageal adenocarcinoma tumors to identify cell states and their spatial interactions, linking them to clinical outcomes.

CDState infers malignant cell subpopulations from bulk RNA-seq data, revealing gene programs and clinical associations in 33 cancer types.

The paper investigates the impact of mitochondrial content filtering in cancer single-cell studies, finding that high pctMT malignant cells are viable and functionally significant.

The paper introduces ANS, a new gene signature scoring method for scRNA-seq, improving cell annotation by addressing limitations in existing methods and demonstrating strong classification performance.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

CanSig benchmarks methods for identifying transcriptional signatures in cancer using scRNA-seq data, finding BBKNN as a leading method with clinically relevant outcomes.