---
dataset_info:
  features:
  - name: Sequences
    dtype: string
  - name: Classes
    dtype: int64
  - name: Proteins
    dtype: string
  splits:
  - name: test
    num_bytes: 9104480
    num_examples: 190955
  download_size: 5167867
  dataset_size: 9104480
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: cc
---

# Detectability - Wang

The dataset contains systematic, quantitative and deep proteome and transcriptome abundance atlas from 29 paired healthy human to serve as a molecular baseline to study human biology. 



## Dataset Details

- **Curated by:** Aalborg University - Denmark, in collaboration with Wilhelmlab, TU Munich.
- **License:** [CC0](https://creativecommons.org/public-domain/cc0/)

### Dataset Sources

The data is based on the datasets introduced in [[1]](#ref1) and available at: https://www.ebi.ac.uk/pride/archive/projects/PXD010154

## Uses

The dataset is used for testing detectability prediction models. However, users are free to use or combine the dataset with other datasets for training, fine-tuning, and testing Detectability models.


## References

<a id="ref1">[1]</a> Wang, D., Eraslan, B., Wieland, T., Hallström, B., Hopf, T., Zolg, D. P., ... & Kuster, B. (2019). A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Molecular systems biology, 15(2), e8503.


## Citation

**BibTeX:**

```bibtex
@article {Abdul-Khalek2024.10.28.620610,
	author = {Abdul-Khalek, Naim and Picciani, Mario and Wimmer, Reinhard and Overgaard, Michael Toft and Wilhelm, Mathias and Echers, Simon Gregersen},
	title = {To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry},
	elocation-id = {2024.10.28.620610},
	year = {2024},
	doi = {10.1101/2024.10.28.620610},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610},
	eprint = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610.full.pdf},
	journal = {bioRxiv}
}
```

**APA:**

Abdul-Khalek, N., Picciani, M., Wimmer, R., Overgaard, M. T., Wilhelm, M., & Gregersen Echers, S. (2024). To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry. bioRxiv, 2024-10.‏

## Dataset Card Contact

Simon Gregersen, sgr@bio.aau.dk, Department of Chemistry and Biosciences, Aalborg University.

Mathias Wilhelm, mathias.wilhelm@tum.de, Wilhelmlab, TU Munich, School of Life Sciences, Germany.