UTHealth Houston

Sequence-conditioned generative framework that reconstructs missing prostate MRI contrasts and restores artefact-degraded acquisitions.

Single-cell perturbation response prediction by conditional latent diffusion, trained on the Tahoe-100M atlas of 100 million drug-treated cells.

Spatial proteomics foundation model trained on over 51 million single cells to learn panel-robust cell representations across platforms.

Generative model that restores cytoplasm-enriched genes lost in snRNA-seq, recovering cell-cell communication signals from raw nuclear counts.

Controllable DNA sequence design conditioned on cell type, transcription factor, or activity signal, in GPT- and BERT-style transformer variants.

Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.

Drug pair synergy prediction for rare cancer tissues, read from a language model's representation of a screening row written out as a sentence.