Anne Hartebrodt

Concurrent de novo inference of cell types and gene regulatory networks with NetMap

NetMap jointly infers cell-level gene regulatory networks and clusters cells based on shared regulatory programs, outperforming existing methods on synthetic data and reliably detecting regulons on real data.

SignifiKANTE: Efficient P-value computation for gene regulatory networks

SignifiKANTE computes efficient P-values for gene regulatory networks by clustering target genes to reduce computational cost.

TNF-α disrupts the malate-aspartate shuttle, driving metabolic rewiring in iPSC-derived enteric neural lineages from Parkinson's Disease patients

This study shows TNF-α disrupts the malate-aspartate shuttle in iPSC-derived enteric neural cells from PD patients, causing metabolic shifts and mitochondrial dysfunction, which can be reversed by targeting glutamate metabolism.

AlterNet: Alternative splicing-aware gene regulatory network inference

AlterNet infers transcript-level gene regulatory networks by considering alternative splicing, revealing novel regulatory interactions not detectable at the gene-level in heart tissue data.

Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65

This study shows that sequence-based PPI prediction models using ESM-2 embeddings plateau at 0.65 accuracy, suggesting structural information may be needed for better performance.

Identifying transcription factors driving cell differentiation

This paper introduces SwitchTFI, a method to identify transcription factors driving cell differentiation using single-cell RNA sequencing data, and shows it outperforms existing methods in functional coherence.

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma

The paper analyzes spatial protein abundance data from skin tissue samples to identify characteristic spatial organization patterns in CTCL compared to AD and PSO.

High-accuracy prediction of 5-year progression-free melanoma survival using spatial proteomics and few-shot learning on primary tissue

This paper predicts 5-year progression-free survival in melanoma using spatial proteomics and few-shot learning, achieving over 90% accuracy.