{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Wayment-Steele HK"],"funding":["NIGMS NIH HHS"],"pubmed_abstract":["Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (\"Stanford OpenVaccine\") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 month"],"journal":["ArXiv"],"pagination":["arXiv:2110.07531v2"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8528079"],"repository":["biostudies-literature"],"pubmed_title":["Deep learning models for predicting RNA degradation via dual crowdsourcing."],"pmcid":["PMC8528079"],"funding_grant_id":["R35 GM122579"],"pubmed_authors":["Steenwinckel B","Participants E","Ito T","Das R","Vandewiele G","Gao J","Mao H","Wayment-Steele HK","Tunguz B","Fujikawa K","Kim DS","Kladwang W","Ishi K","Romano J","Fares M","Wellington-Oguri R","Ozturk F","Tinti M","Ozturk E","Watkins AM","Demkin M","Chiu A","He S","Onodera K","Noumi T","Reade W","Lee Y","Nicol JJ","Amer K"],"additional_accession":[]},"is_claimable":false,"name":"Deep learning models for predicting RNA degradation via dual crowdsourcing.","description":"Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (\"Stanford OpenVaccine\") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 month","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2026-05-31T03:15:13.601Z","creation":"2025-04-06T08:09:50.353Z"},"accession":"S-EPMC8528079","cross_references":{"pubmed":["34671698"]}}