Instructions to use simiaoAA/cross-species-aging-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use simiaoAA/cross-species-aging-models with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://simiaoAA/cross-species-aging-models") - Notebooks
- Google Colab
- Kaggle
Clean model card
Browse files
README.md
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---
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license:
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library_name: keras
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tags:
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- keras
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- tensorflow
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- single-cell
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- aging
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- domain-adaptation
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- cross-species
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pretty_name: Cross-species
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---
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# Cross-
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This model repository
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## Expected Inputs
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Inputs must be aligned to the feature files distributed with the dataset repository. See the GitHub repository for scripts:
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- `scripts/evaluate_scrna_dann.py`
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- `scripts/search_hsc_dann_saved_models.py`
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- `scripts/train_scrna_dann_tf.py`
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## Limitations
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Adversarial training is checkpoint-sensitive. Saved weights should be used with the exact feature lists, scaling choices and class order described in the GitHub documentation.
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## Citation
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Please cite the manuscript and the archived model DOI when available.
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---
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license: mit
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library_name: keras
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tags:
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- keras
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- tensorflow
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- single-cell
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- aging
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- domain-adaptation
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- cross-species
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pretty_name: Cross-species aging DANN models
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---
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# Cross-Species Aging DANN Models
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This model repository stores trained Keras/TensorFlow model files required by
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the GitHub reproducibility package:
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https://github.com/SimiaoZhao/cross-species-aging-resource
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Inputs must be aligned to the feature lists distributed with the dataset
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repository. During peer review, access may be restricted to reviewers and
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editors.
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