Payman Yadollahpour

This paper introduces UTMM, a method to simultaneously measure transcriptomes and mechanical properties in intact embryos, revealing coupling between RNA expression and intracellular compliance during early development.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

EquiFold predicts all-atom protein structures using an SE(3)-equivariant coarse-grained geometric representation without sequence alignments or language model embeddings, matching AlphaFold accuracy orders of magnitude faster in preliminary tests.

This study uses single-nucleus and spatial transcriptomics to analyze pancreatic cancer before and after neoadjuvant treatment, revealing changes in cell states and immune infiltration that may inform therapy.

The paper generates a single-cell RNA-seq and ATAC-seq atlas of the developing mouse neocortex and reconstructs developmental trajectories to infer gene regulatory programs and identify lineage-specific developmental abnormalities.