A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

A drop-in modernization of bowang-lab/scGPT for Python 3.12 + torch 2.6 + flash-attn 3 (H100 sm_90a native). Original pretrained weights load unmodified — compatible, more modern, faster.

Reproducible pipelines comparing bulk RNA-seq, single-cell (pseudobulk), and scFM embeddings (scGPT, scFoundation) for per-drug response prediction in cancer cell lines.

Companion code for the PLOS ONE paper 'What topological and geometric structure do biological foundation models learn? Evidence from 141 hypotheses'.

scGPT fine-tuning on PBMC3k · 7-method benchmark · Nature Methods 2024 framework

Causal token interventions for validating gene regulatory network inference from single-cell foundation models

Автосервис в Минске. Качественный ремонт авто.