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Upload app.py

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  1. app.py +8 -4
app.py CHANGED
@@ -82,8 +82,13 @@ __Note: Inference may take a loger time, you can upgrade to a paid GPU-enabled p
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  """
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  article = """
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- This interface enables the use of <span style='font-variant:small-caps;'>BarlowDTI</span> to predict drug-target interactions.
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- The model ensemble consists of three models trained on different datasets: BindingDB, BIOSNAP, and DAVIS.
 
 
 
 
 
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  If you use this interface in your research, please cite our paper:
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  ```
@@ -94,7 +99,7 @@ If you use this interface in your research, please cite our paper:
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  eprint={2408.00040},
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  archivePrefix={arXiv},
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  primaryClass={q-bio.BM},
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- url={https://arxiv.org/abs/2408.00040},
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  }
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  ```
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  """
@@ -114,7 +119,6 @@ iface = gr.Interface(
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  outputs=[
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  gr.Plot(label="Predictions Visualization"),
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  gr.DataFrame(label="Predictions DataFrame"),
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- # gr.DownloadButton(label="Download Predictions")
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  ],
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  title=title,
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  description=description,
 
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  """
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  article = """
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+ This interface enables the use of <span style='font-variant:small-caps;'>BarlowDTI</span><sub>XXL</sub> to predict drug-target interactions.
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+ The model ensemble consists of four models trained on different datasets: our own curated and refined dataset based on
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+ [Golts et. al](https://doi.org/10.48550/arXiv.2401.17174)
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+ in combination with
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+ [BindingDB](https://doi.org/10.1093/nar/gkl999),
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+ [BioSNAP](https://snap.stanford.edu/index.html), and
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+ [DAVIS](https://doi.org/10.1038/nbt.1990).
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  If you use this interface in your research, please cite our paper:
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  ```
 
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  eprint={2408.00040},
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  archivePrefix={arXiv},
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  primaryClass={q-bio.BM},
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+ url={https://arxiv.org/abs/2408.00040},
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  }
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  ```
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  """
 
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  outputs=[
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  gr.Plot(label="Predictions Visualization"),
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  gr.DataFrame(label="Predictions DataFrame"),
 
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  ],
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  title=title,
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  description=description,