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
Dataset Sources
The data is based on the datasets introduced in [1] 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
[1] 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:
@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.