Datasets:

GrimSqueaker
/

ProFET_NP_SP_Cleaved

language:
  - en
license: other
tags:
  - biology
  - proteins
  - sequence-classification
  - benchmark
task_categories:
  - text-classification
pretty_name: ProFET_NP_SP_Cleaved

ProFET_NP_SP_Cleaved

Binary benchmark for neuropeptide precursor prediction from protein sequences, adapted from the ProteinBERT benchmark collection.

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/ProFET_NP_SP_Cleaved

Citations

@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}
}

@article{OferD2014,
    author = {Ofer, Dan and Linial, Michal},
    title = {NeuroPID: a predictor for identifying neuropeptide precursors from metazoan proteomes},
    journal = {Bioinformatics},
    volume = {30},
    number = {7},
    pages = {931--940},
    year = {2014},
    doi = {10.1093/bioinformatics/btt725}
}

@article{Karsenty2014,
    author = {Karsenty, S. and Rappoport, N. and Ofer, D. and Zair, A. and Linial, M.},
    title = {NeuroPID: a classifier of neuropeptide precursors},
    journal = {Nucleic Acids Research},
    year = {2014},
    doi = {10.1093/nar/gku363}
}

@article{Brandes2016,
    author = {Brandes, Nadav and Ofer, Dan and Linial, Michal},
    title = {ASAP: A machine learning framework for local protein properties},
    journal = {Database},
    volume = {2016},
    year = {2016},
    doi = {10.1093/database/baw133}
}