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@@ -78,6 +78,10 @@ For usage, see [examples](https://huggingface.co/ctheodoris/Geneformer/tree/main
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  - extracting and plotting cell embeddings
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  - in silico perturbation
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  Please note that the fine-tuning examples are meant to be generally applicable and the input datasets and labels will vary dependent on the downstream task. Example input files for a few of the downstream tasks demonstrated in the manuscript are located within the [example_input_files directory](https://huggingface.co/datasets/ctheodoris/Genecorpus-30M/tree/main/example_input_files) in the dataset repository, but these only represent a few example fine-tuning applications.
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  Please note that GPU resources are required for efficient usage of Geneformer. Additionally, we strongly recommend tuning hyperparameters for each downstream fine-tuning application as this can significantly boost predictive potential in the downstream task (e.g. max learning rate, learning schedule, number of layers to freeze, etc.). Importantly, as usual for deep learning models, there are no uniformly applicable default hyperparameters for Geneformer.
 
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  - extracting and plotting cell embeddings
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  - in silico perturbation
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+ Please also see [here](https://tinyurl.com/geneformertutorial) for a quickstart tutorial for predicting candidate therapeutic targets with Geneformer.
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+ Complete documentation is available at https://geneformer.readthedocs.io/en/latest/.
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  Please note that the fine-tuning examples are meant to be generally applicable and the input datasets and labels will vary dependent on the downstream task. Example input files for a few of the downstream tasks demonstrated in the manuscript are located within the [example_input_files directory](https://huggingface.co/datasets/ctheodoris/Genecorpus-30M/tree/main/example_input_files) in the dataset repository, but these only represent a few example fine-tuning applications.
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  Please note that GPU resources are required for efficient usage of Geneformer. Additionally, we strongly recommend tuning hyperparameters for each downstream fine-tuning application as this can significantly boost predictive potential in the downstream task (e.g. max learning rate, learning schedule, number of layers to freeze, etc.). Importantly, as usual for deep learning models, there are no uniformly applicable default hyperparameters for Geneformer.