license: apache-2.0
dataset_info:
config_name: PDB_Structures
features:
- name: name
dtype: string
- name: source
dtype: string
- name: PDB
dtype: string
splits:
- name: train
num_bytes: 363960255
num_examples: 1724
download_size: 188614040
dataset_size: 363960255
configs:
- config_name: PDB_Structures
data_files:
- split: train
path: PDB_Structures/data/train-*
FoldDock
Use AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure.>
Quick Start Usage
Install Hugging Face Datasets Packages
Each subset can be loaded into python using the Huggingface datasets library.
First, from the command line install the datasets library
$ pip install datasets
Optionally set the cache directory, e.g. $ HF_HOME=${HOME}/.cache/huggingface/ $ export HF_HOME then, from within python load the datasets library >>> import datasets
Load Model Datasets
To load one of the <rmauder/FoldDock> model datasets, use datasets.load_dataset(...):
>>> dataset_tag = "<DATASET TAG>" >>> dataset = datasets.load_dataset( path = "<HF PATH TO DATASET>", name = f"{dataset_tag}", data_dir = f"{dataset_tag}")['train']
and the dataset is loaded as a datasets.arrow_dataset.Dataset
>>> dataset <RESULT OF LOADING DATASET MODEL>
which is a column oriented format that can be accessed directly, converted in to a pandas.DataFrame, or parquet format, e.g.
>>> dataset.data.column('<COLUMN NAME IN DATASET>') >>> dataset.to_pandas() >>> dataset.to_parquet("dataset.parquet")
Dataset Details
Dataset Description
Acknowledgements:
License: apache-2.0
Dataset Sources - Repository: https://doi.org/10.17044/scilifelab.16866202.v1 - Paper: References
Bryant, P., Pozzati, G., & Elofsson, A. (2021). Data and most relevant results for the FoldDock project [Data set]. doi:10.17044/SCILIFELAB.16866202.V1
Uses
Out-of-Scope Use
Source Data
Citation
@misc{https://doi.org/10.17044/scilifelab.16866202.v1, doi = {10.17044/SCILIFELAB.16866202.V1}, url = {https://figshare.scilifelab.se/articles/dataset/Data_and_most_relevant_results_for_the_FoldDock_project/16866202/1}, author = {Bryant, Patrick and Pozzati, Gabriele and Elofsson, Arne}, keywords = {Bioinformatics and computational biology not elsewhere classified}, title = {Data and most relevant results for the FoldDock project}, publisher = {Stockholm University}, year = {2021}, copyright = {Apache 2.0} }