Ruth Dannenfelser

Cavebear improves pseudotime inference by using reference species time-series data to guide ordering of cells in query species or conditions without time labels.

The paper analyzes alternative splicing's impact on protein interaction networks across cancers, identifying structured rewiring patterns and their association with patient survival.

The paper introduces an ontology-aware classification framework for DNA methylation data using a curated atlas of healthy human tissues and cell types, identifying CpG sites that accurately classify tissues and cell types.

ALPINE is a supervised NMF framework that disentangles technical and phenotypic factors in multi-condition sequencing data, improving condition-specific signal isolation and interpretability.

This study benchmarks 38 gene embedding methods across three functional prediction tasks, finding literature-based embeddings perform best overall, while sequence-based models excel in genetic interactions.

Seismic identifies cell-type-specific associations with complex traits by integrating scRNA-seq and GWAS, revealing trait-relevant cell groups and gene drivers.

Splitpea quantifies changes in protein interaction networks due to alternative splicing in cancer by analyzing domain-domain interactions and differential exon usage data from TCGA and GTEx.

ANDES enhances gene set analysis in embedding spaces by comparing gene sets and achieving state-of-the-art performance in enrichment analysis.

This paper introduces ETNA, a joint embedding method for cross-species functional alignment using biological networks and sequence orthologs, showing improved preservation of gene functional relationships.

This study identifies a functional gene module associated with selective neuronal vulnerability in Alzheimer’s disease using integrated molecular and genetic data from mice and humans.