Helmholtz Munich

Genetically aligned foundation model for blood smear cytology that links single white-blood-cell morphology to chromosomal aberrations and mutations for AML/APL diagnosis.

Bayesian deep generative model that integrates gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation of transcription factors.

Latent flow-matching foundation model that predicts pan-cancer spatially-resolved single-cell gene expression directly from routine H&E histology slides.

A protein language model tool that predicts per-residue local energetic frustration directly from sequence, enabling proteome-scale frustration analysis in minutes.

A LoRA adapter on ProstT5 that predicts per-residue probability distributions over Foldseek 3Di tokens, capturing sequence-encoded conformational flexibility from MD trajectories.

Transformer-based contrastive pretraining framework that learns technology-agnostic single-cell representations by contrasting cell views instead of reconstructing gene expression.

Predicts single-cell-resolution scRNA-seq coverage and scATAC-seq insertion profiles directly from DNA sequence by adapting the Borzoi predictor with a cell-specific decoder.

Transformer foundation model pretrained on 110M single-cell and spatially resolved transcriptomics profiles, enabling spatial context prediction for dissociated cells.