Datasets:
File size: 1,735 Bytes
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language:
- en
license: other
tags:
- biology
- proteins
- sequence-classification
- benchmark
task_categories:
- text-classification
pretty_name: SignalP_Binary
---
# SignalP_Binary
Binary benchmark for signal peptide prediction from protein sequences, adapted from the ProteinBERT benchmark collection and SignalP data.
## Source
This dataset is sourced from the ProteinBERT benchmark repository:
https://github.com/nadavbra/protein_bert/tree/master/protein_benchmarks
## Curator Attribution
This Hugging Face dataset packaging, curation, and publication was prepared by Dan Ofer.
## Splits and Schema
- Splits follow the benchmark release (train/validation/test when available).
- Each row includes:
- seq: amino-acid sequence
- label: binary target (0 or 1)
## Hugging Face Repo
- GrimSqueaker/SignalP_Binary
## Citations
```bibtex
@article{Armenteros2019,
author = {Almagro Armenteros, Jose Juan and Tsirigos, Konstantinos D. and Sonderby, Casper Kaae and Petersen, Thomas Nordahl and Winther, Ole and Brunak, Soren and von Heijne, Gunnar and Nielsen, Henrik},
title = {SignalP 5.0 improves signal peptide predictions using deep neural networks},
journal = {Nature Biotechnology},
volume = {37},
number = {4},
pages = {420--423},
year = {2019},
doi = {10.1038/s41587-019-0036-z}
}
@article{10.1093/bioinformatics/btac020,
author = {Brandes, Nadav and Ofer, Dan and Peleg, Yam and Rappoport, Nadav and Linial, Michal},
title = {ProteinBERT: a universal deep-learning model of protein sequence and function},
journal = {Bioinformatics},
volume = {38},
number = {8},
pages = {2102-2110},
year = {2022},
doi = {10.1093/bioinformatics/btac020}
}
```
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