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license: cc-by-4.0
task_categories:
- text-regression
language:
- en
tags:
- biology
- dna
- genomics
- promoter
- gene-expression
- carbon
---
# Random Promoter DREAM Challenge 2022
This dataset repackages the processed Random Promoter DREAM Challenge 2022
files from Zenodo record `10633252` for use with `datasets`.
The task is sequence-to-expression regression on synthetic yeast promoter
sequences. The canonical supervised config contains random promoter training
examples, validation examples, and labeled designed test promoters.
## Configs
- `supervised`: `train`, `validation`, and `test` splits with promoter
sequences and measured activity.
- `challenge_test_sequences`: unlabeled test sequences for submission-style
prediction workflows.
- `test_subset_membership`: normalized IDs from `test_subset_ids.tar.gz`.
- `public_leaderboard_ids`: normalized IDs from `public_leaderboard_ids.tar.gz`.
## Schema
`supervised`:
- `sequence`: DNA sequence.
- `activity`: measured promoter activity.
- `sequence_length`: sequence length in base pairs.
- `source_file`: source filename.
- `row_id`: zero-based row index within the source split.
ID metadata configs:
- `subset`: subset name inferred from the archive member.
- `item_id`: raw ID token from the source line.
- `row_id`: integer row ID when `item_id` is numeric; otherwise `-1`.
- `source_member`: archive member path.
- `line_number`: line number within the member.
- `raw_line`: unmodified stripped source line.
## Usage
```py
from datasets import load_dataset
ds = load_dataset("HuggingFaceBio/random-promoter-dream-2022", "supervised")
train = ds["train"]
validation = ds["validation"]
test = ds["test"]
subsets = load_dataset("HuggingFaceBio/random-promoter-dream-2022", "test_subset_membership", split="train")
```
## Source
Source: Random Promoter DREAM Challenge 2022, Zenodo DOI
`10.5281/zenodo.10633252`.
The source record is licensed CC BY 4.0. Cite the original DREAM Challenge data
and paper when using this dataset.
## Reproduction
This dataset repo includes `create_dataset.py`, the script used to download,
convert, and upload the configs.
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