Text Classification
Transformers
PyTorch
bert
protein language model
biology
text-embeddings-inference
Instructions to use GleghornLab/SYNTERACT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleghornLab/SYNTERACT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GleghornLab/SYNTERACT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GleghornLab/SYNTERACT") model = AutoModelForSequenceClassification.from_pretrained("GleghornLab/SYNTERACT") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Intended use and limitations
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We define a protein-protein interaction as physical contact that mediates chemical or conformational change, especially with non-generic function. However, due to
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## Our lab
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The [Gleghorn lab](https://www.gleghornlab.com/) is an
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## Please cite
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@article {Hallee_ppi_2023,
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```
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## Intended use and limitations
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We define a protein-protein interaction as physical contact that mediates chemical or conformational change, especially with non-generic function. However, due to SYNTERACT's propensity to predict false positives, we believe that it identifies plausible conformational changes caused by interactions without relevance to function.
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## Our lab
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The [Gleghorn lab](https://www.gleghornlab.com/) is an interdisciplinary research group at the University of Delaware that focuses on solving translational problems with our expertise in engineering, biology, and chemistry. We develop inexpensive and reliable tools to study organ development, maternal-fetal health, and drug delivery. Recently we have begun exploration into protein language models and strive to make protein design and annotation accessible.
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## Please cite
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@article {Hallee_ppi_2023,
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