PanMET is a genome-aware foundation-model framework for RNA sequencing and DNA methylation. It combines independently pretrained modality towers, leakage-free cross-modal alignment, and masked bidirectional cross-modal reconstruction.

This repository contains the retained canonical model weights in the safe, tensor-only safetensors format:

The original training checkpoints contained optimizer and scheduler state. Those training-only objects are intentionally excluded. Patient split manifests are also excluded. manifest.json records SHA-256 hashes for every source checkpoint and exported artifact, and every exported tensor was checked for exact equality after serialization.

PanMET uses a custom PyTorch architecture from the PanMET source repository. Download a component and load its state dictionary with safetensors:

Stage C reconstruction uses stage_c/rna_decoder.safetensors and stage_c/methylation_decoder.safetensors in addition to the Stage C model. Architecture construction, preprocessing, and inference adapters are provided in the source repository.

Input feature identity, order, and preprocessing must match the training caches. The model is intended for research use and is not a clinical device.

The PanMET manuscript is in preparation. Citation information will be updated when it becomes available.