Meghana Kshirsagar

AtomWorks, a modular atom-level data framework, trains structure prediction and design models; the resulting RF3 improves chirality handling and performs between AF3 and Boltz.

The paper uses spatial transcriptomics and a new method called DeepTopics to identify distinct cellular patterns in RA synovium, revealing divergent ectopic lymphoid structures.

This paper applies parameter-efficient fine-tuning methods to proteomics, achieving competitive or better performance than traditional fine-tuning with significantly reduced computational resources.

Chromatin regulators including EZH2, EHMT1/2, and DOT1L establish an epigenetic barrier in human neural progenitors that poises maturation genes and sets the protracted pace of cortical neuron maturation.

BindVAE is a deep learning method for discovering TF motifs from open chromatin data, achieving higher precision than existing methods but with lower recall.

BindSpace is a model that embeds DNA sequences and TF labels into a shared space, achieving state-of-the-art binding prediction for hundreds of TFs.