gtCLIP — Gene-set Text CLIP

A CLIP-style model that aligns gene set embeddings (via GSFM) with biomedical text embeddings (via S-PubMedBert-MS-MARCO) in a shared latent space.

Quick Start

GIT_LFS_SKIP_SMUDGE=1 pip install git+https://huggingface.co/maayanlab/gsfm
pip install sentence-transformers huggingface_hub

from huggingface_hub import hf_hub_download
import importlib.util

py_path = hf_hub_download("DaveLab/gtCLIP", "gtCLIP.py")
spec = importlib.util.spec_from_file_location("gtCLIP", py_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
GeneSetTextEncoder = mod.GeneSetTextEncoder

model = GeneSetTextEncoder.from_pretrained("DaveLab/gtCLIP", device="cuda")

# Encode a gene set
gene_emb = model.encode_gene_symbols(["TP53", "BRCA1", "MYC", "CDK2"])

# Encode a text description
text_emb = model.encode_text_normalized(["DNA damage repair pathway"])

# Compute similarity
similarity = (gene_emb @ text_emb.T).item()
print(f"Similarity: {similarity:.4f}")

Files

File	Description
gtCLIP.py	Model class definition
gtCLIP.pt	Trained weights + config
config.json	Architecture hyperparameters

Architecture

• Gene encoder: GSFM (frozen or partially fine-tuned) → projection MLP → shared space
• Text encoder: PubMedBERT (frozen or partially fine-tuned) → projection MLP → shared space
• Training: Contrastive CLIP loss with gene-set overlap soft targets + reconstruction auxiliary loss