ChatterjeeLab/PIVOT

A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.

GeneJEPA is a Joint-Embedding Predictive Architecture (JEPA) trained for self-supervised representation learning on scRNA-seq. It uses a Perceiver-style encoder to handle sparse, high-dimensional gene count vectors and a Fourier-feature tokenizer for numerical tokenization.

Tahoe-x1 is a family of perturbation-trained single-cell foundation models with up to 3 billion parameters, developed by Tahoe Therapeutics. Pretrained on 266 million single-cell transcriptomic profiles including the Tahoe-100M perturbation compendium, Tahoe-x1 achieves state-of-the-art performance…

Senba is a source-anchored adaptation of MarS-FM (Valence Labs, ICLR 2026) for mdCATH backbone transitions at 450 K and one 50-frame lag. It is published as a validation-stage candidate together with the selection receipt, every comparison, and the preregistered protocols, so the claim below can be…

This repository contains the PyTorch model weights for MarS-FM (Markov Space Flow Matching) trained on the MD-CATH dataset. This model was introduced in the ICLR 2026 paper: MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models.

Universal Cell Embeddings (UCE) is a foundation model designed for single-cell RNA sequencing data analysis. UCE generates a universal representation of cells that captures the molecular diversity across different cell types, tissues, and species.