Shentong Mo

scLong is a billion-parameter foundation model that captures long-range gene context in scRNA-seq by modeling all genes and integrating gene knowledge, outperforming existing models in various tasks.

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.

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

The paper introduces AIDO.Cell, a series of dense Transformer models pre-trained on human single-cell RNA-seq data, achieving state-of-the-art results in clustering, classification, and perturbation modeling.

AttentionPert predicts gene expression under multiplexed genetic perturbations and generalizes to unseen conditions, outperforming existing methods in accuracy and handling diverse perturbations.