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--- |
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tags: |
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- bioinformatics |
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- gene |
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- gene set |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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--- |
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# GSFM |
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Trained on millions of gene sets automatically extracted from literature and raw RNA-seq data, GSFM learns to recover held-out genes from gene sets. The resulting model exhibits state of the art performance on gene function prediction. |
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**Deprecation Notice**: This repo was replaced with <https://github.com/MaayanLab/gsfm> -- you can now access different versions of the model, stored on huggingface, directions in that repository. |
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## Website |
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<https://gsfm.maayanlab.cloud/> |
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## Usage |
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```bash |
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# install gsfm python library from its source on huggingface |
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GIT_LFS_SKIP_SMUDGE=1 pip install git+https://huggingface.co/maayanlab/gsfm |
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``` |
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```python |
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import torch |
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from gsfm import Vocab, GSFM |
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# load gsfm vocabulary and model weights |
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vocab = Vocab.from_pretrained('maayanlab/gsfm') |
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gsfm = GSFM.from_pretrained('maayanlab/gsfm') |
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gsfm.eval() |
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# convert gene symbols into token ids |
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token_ids = torch.tensor(vocab(['ACE1', 'ACE2']))[None, :] |
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# use model to predict missing genes from the set |
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logits = torch.squeeze(gsfm(token_ids)) |
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top_10 = sorted(zip(logits, vocab.vocab))[-10:] |
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top_10 |
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# get model middle layer |
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gene_set_encoding = gsfm.encode(token_ids) |
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gene_set_encoding |
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``` |
