Deep learning models for predicting RNA degradation via dual crowdsourcing.

Hannah K Wayment-Steele, Wipapat Kladwang, Andrew M Watkins, Do Soon Kim, Bojan Tunguz, Walter Reade, Maggie Demkin, Jonathan Romano, Roger Wellington-Oguri, John J Nicol, Jiayang Gao, Kazuki Onodera, Kazuki Fujikawa, Hanfei Mao, Gilles Vandewiele

December 2022 Nat Mach Intell

Synopsis of Social media discussions

The discussions highlight excitement over the potential applications of the developed models, with phrases such as 'game changer' and 'valuable piece' to emphasize importance. Additionally, references to the OpenVaccine competition indicate a sense of community and collective effort in the scientific field, which underscores the significance of collaborative approaches in research.

A
Agreement
 Moderate agreement

Many discussions revolve around the publication and its innovative approach, indicating a general endorsement of its findings.

I
Interest
 High level of interest

Posts express strong enthusiasm about the research topic, particularly regarding its relevance to current scientific challenges.

E
Engagement
 Moderate level of engagement

Some posts involve references to specific details about the competition and methodologies used, though not deeply analytical.

I
Impact
 High level of impact

There is a clear belief that the publication could have a substantial influence on RNA research and mRNA-based medicines.

Social Mentions

YouTube

2 Videos

Twitter

92 Posts

Blogs

3 Articles

Metrics

Video Views

33

Total Likes

190

Extended Reach

1,033,993

Social Features

97

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Posts referencing the article

デュアルクラウドソーシングによるRNA分解予測のためのディープラーニングモデル(アブストラクト)

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26 views

Predicting RNA Degradation Using Deep Learning Techniques

Predicting RNA Degradation Using Deep Learning Techniques

This video explores the challenges in mRNA stability affecting therapeutics like COVID-19 vaccines, highlighting a successful machine learning model from the Stanford OpenVaccine competition. It discusses the use of dual crowdsourcing for data collection and model training in RNA degradation predictions.

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October 19, 2023

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Abstract Synopsis

  • mRNA-based medicines, like COVID-19 vaccines, have great potential but face challenges with stability due to their susceptibility to chemical degradation.
  • A machine learning competition called 'Stanford OpenVaccine' on Kaggle involved analyzing over 6,000 RNA constructs to predict their degradation, resulting in a successful model that accurately forecasted RNA stability.
  • The integration of crowdsourcing for both data collection and model training could be a beneficial approach for quickly addressing other scientific challenges in the future.