| --- |
| 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} |
| } |
| ``` |
|
|
