metadata

language: rna
tags:
  - Biology
  - RNA
license:
  - agpl-3.0
size_categories:
  - 1K<n<10K
task_categories:
  - text-generation
  - fill-mask
task_ids:
  - language-modeling
  - masked-language-modeling
pretty_name: RYOS
library_name: multimolecule

RYOS

RYOS is a database of RNA backbone stability in aqueous solution.

Statement

Deep learning models for predicting RNA degradation via dual crowdsourcing is published in Nature Machine Intelligence, which is a Closed Access / Author-Fee journal.

Machine learning has been at the forefront of the movement for free and open access to research.

We see no role for closed access or author-fee publication in the future of machine learning research and believe the adoption of these journals as an outlet of record for the machine learning community would be a retrograde step.

The MultiMolecule team is committed to the principles of open access and open science.

We do NOT endorse the publication of manuscripts in Closed Access / Author-Fee journals and encourage the community to support Open Access journals and conferences.

Please consider signing the Statement on Nature Machine Intelligence.

Disclaimer

This is an UNOFFICIAL release of the RYOS by Hannah K. Wayment-Steele et al.

The team releasing RYOS did not write this dataset card for this dataset so this dataset card has been written by the MultiMolecule team.

Dataset Description

Homepage: https://multimolecule.danling.org/datasets/ryos
Point of Contact: Rhiju Das
Kaggle Challenge: https://www.kaggle.com/competitions/stanford-covid-vaccine
Eterna Round 1: https://eternagame.org/labs/9830365
Eterna Round 2: https://eternagame.org/labs/10207059

Example Entry

TODO

Column Description

TODO

Variations

This dataset is available in two subsets:

RYOS-1: The RYOS-1 dataset.
RYOS-2: The RYOS-2 dataset.

License

This dataset is licensed under the AGPL-3.0 License.

SPDX-License-Identifier: AGPL-3.0-or-later

Citation

@article{waymentsteele2021,
  author  = {Wayment-Steele, Hannah K and Kladwang, Wipapat and Watkins, Andrew M and Kim, Do Soon and Tunguz, Bojan and Reade, Walter and Demkin, Maggie and Romano, Jonathan and Wellington-Oguri, Roger and Nicol, John J and Gao, Jiayang and Onodera, Kazuki and Fujikawa, Kazuki and Mao, Hanfei and Vandewiele, Gilles and Tinti, Michele and Steenwinckel, Bram and Ito, Takuya and Noumi, Taiga and He, Shujun and Ishi, Keiichiro and Lee, Youhan and {\"O}zt{\"u}rk, Fatih and Chiu, Anthony and {\"O}zt{\"u}rk, Emin and Amer, Karim and Fares, Mohamed and Participants, Eterna and Das, Rhiju},
  journal = {ArXiv},
  month   = oct,
  title   = {Deep learning models for predicting {RNA} degradation via dual crowdsourcing},
  year    = 2021
}