Jimin Tan

The paper identifies ZNF654 and JMJD6 as novel factors defining TAD boundaries through in silico screens and experimental validation.

This paper introduces a segmentation-free method for analyzing multispectral whole slide images to study tissue repair and regeneration, revealing tissue compartments and dynamics without prior labels.

Chromnitron predicts protein-DNA binding landscapes across cell types and proteins using multimodal learning, revealing new regulators of T cell exhaustion and dynamic shifts during neurogenesis.

This paper introduces a segmentation-free self-supervised AI model to analyze brain tumor microenvironments, identifying a novel tumor-associated B cell subset linked to improved survival.

The paper introduces a segmentation-free vision transformer, CANVAS, to discover and characterize tumor microenvironment signatures from high-dimensional multiplexed images, identifying a monocytic signature linked to poor prognosis.

GET is a foundation model that accurately predicts gene expression and regulatory interactions across 213 human cell types using chromatin accessibility and sequence data.

This study introduces C.Origami, a deep learning model that predicts cell type-specific 3D chromatin organization, enabling in silico genetic screening to identify genomic regions affecting chromatin structure.