proteins
Datasets
All datasets matching “proteins”initial-dynamic-proteins
Initial 2,000-protein dataset
This is the canonical local root for the first complete router dataset: 1,000
single-dominant structured-state proteins (label 0) and 1,000 dynamic or
heterogeneous-state proteins (label 1). The fixed split is 1,400 train, 300
validation, and 300 test proteins.
Place Colab's completed ESMFold result files (<sequence_sha256>.npz) in
esmfold_results/. Then import them with:
uv run python scripts/esmfold_dataset.py import
The importer validates every… See the full description on the dataset page: https://huggingface.co/datasets/archiitecture/initial-dynamic-proteins.Proteinscasp14-casp15-cameo-test-proteinsprotein_structure_NER_model_v3.1
Overview
This data was used to train model:
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"… See the full description on the dataset page: https://huggingface.co/datasets/PDBEurope/protein_structure_NER_model_v3.1.protein_structure_NER_model_v2.1
Overview
This data was used to train model:
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"… See the full description on the dataset page: https://huggingface.co/datasets/PDBEurope/protein_structure_NER_model_v2.1.ProteinSelfies10 million random examples from Uniref50 representative sequences (October 2023) and computed selfies strings. The strings are stored as input ids from a custom selfies tokenizer. A BERT tokenizer with this vocabulary has been uploaded to this dataset under the files.
You can access the tokenizer like this:
import os
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
repo_path = 'Synthyra/ProteinSelfies'
local_path = 'ProteinSelfies'
files =… See the full description on the dataset page: https://huggingface.co/datasets/Synthyra/ProteinSelfies.