nielsr
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Fix paper URL

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@@ -5,7 +5,7 @@ inference: false
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  # Enformer
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- Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://arxiv.org/abs/2010.11929) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer).
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  This particular model was trained on sequences of 196,608 basepairs, target length 896, with shift augmentation but without reverse complement, on poisson loss objective. Final human pearson R of ~0.49.
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@@ -17,7 +17,7 @@ Disclaimer: The team releasing Enformer did not write a model card for this mode
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  Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.
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- We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x#citeas) published in Nature for details.
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  ### How to use
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  # Enformer
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+ Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer).
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  This particular model was trained on sequences of 196,608 basepairs, target length 896, with shift augmentation but without reverse complement, on poisson loss objective. Final human pearson R of ~0.49.
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  Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.
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+ We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x) published in Nature for details.
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  ### How to use
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