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@@ -13,88 +13,19 @@ The nucleotide_transformer_downstream_tasks_public dataset features 2 of the 18
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  ### Dataset Summary
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- This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
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- ### Supported Tasks and Leaderboards
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- [More Information Needed]
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- ### Languages
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  ## Dataset Structure
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- ### Data Instances
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- ### Data Fields
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- ### Data Splits
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- ## Dataset Creation
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- ### Curation Rationale
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- ### Source Data
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- #### Initial Data Collection and Normalization
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- #### Who are the source language producers?
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- ### Annotations
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- #### Annotation process
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- #### Who are the annotators?
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- ### Personal and Sensitive Information
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- ## Considerations for Using the Data
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- ### Social Impact of Dataset
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- ### Discussion of Biases
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- ### Other Known Limitations
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- ## Additional Information
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- ### Dataset Curators
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- ### Licensing Information
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- ### Citation Information
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- ### Contributions
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  ### Dataset Summary
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+ This dataset features two classification datasets that were introduced in two different genomics papers:
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+ - [DeePromoter: Robust Promoter Predictor Using Deep Learning](https://www.frontiersin.org/articles/10.3389/fgene.2019.00286/full): The datasets features 3,065 TATA promoters and 26,532 non-TATA promoters, with each promoter yielding a negative sequence by randomly sampling parts of the sequence.
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+ - [A deep learning framework for enhancer prediction using word embedding and sequence generation](https://www.sciencedirect.com/science/article/abs/pii/S0301462222000643): To build the training dataset, the authors collect 742 strong
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+ enhancers, 742 weak enhancers and 1484 non-enhancers, and augment the dataset with 6000 synthetic enhancers and 6000 synthetic non-enhancers produced with a generative model. The test dataset is comprised of 100 strong enhancers, 100 weak enhancers and 200 non enhancers. The original paper also uses this dataset to do both binary classification (i.e a sample gets classified as non-enhancer or enhancer) and 3-class classification (i.e a sample gets classified as non-enhancer, weak enhancer or strong enhancer). Here the dataset has been processed to do binary classification only.
 
 
 
 
 
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  ## Dataset Structure
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+ ```
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+ | Task | Number of train sequences | Number of test sequences |
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+ | ------------- | --------------------------| --------------------------|
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+ | promoter_all | 53,276 | 5,920 |
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+ | enhancers | 14,968 | 400 |
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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