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<div class="control is-expanded"> <label for="query" class="hidden-label">Search term or terms</label> <input class="input is-medium" id="query" name="query" placeholder="Search term..." type="text" value="Romano, J"> </div> <div class="select control is-medium"> <label class="is-hidden" for="searchtype">Field</label> <select class="is-medium" id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author 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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.07531">arXiv:2110.07531</a> <span> [<a href="https://arxiv.org/pdf/2110.07531">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Deep learning models for predicting RNA degradation via dual crowdsourcing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wayment-Steele%2C+H+K">Hannah K. Wayment-Steele</a>, <a href="/search/q-bio?searchtype=author&query=Kladwang%2C+W">Wipapat Kladwang</a>, <a href="/search/q-bio?searchtype=author&query=Watkins%2C+A+M">Andrew M. Watkins</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+D+S">Do Soon Kim</a>, <a href="/search/q-bio?searchtype=author&query=Tunguz%2C+B">Bojan Tunguz</a>, <a href="/search/q-bio?searchtype=author&query=Reade%2C+W">Walter Reade</a>, <a href="/search/q-bio?searchtype=author&query=Demkin%2C+M">Maggie Demkin</a>, <a href="/search/q-bio?searchtype=author&query=Romano%2C+J">Jonathan Romano</a>, <a href="/search/q-bio?searchtype=author&query=Wellington-Oguri%2C+R">Roger Wellington-Oguri</a>, <a href="/search/q-bio?searchtype=author&query=Nicol%2C+J+J">John J. Nicol</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+J">Jiayang Gao</a>, <a href="/search/q-bio?searchtype=author&query=Onodera%2C+K">Kazuki Onodera</a>, <a href="/search/q-bio?searchtype=author&query=Fujikawa%2C+K">Kazuki Fujikawa</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+H">Hanfei Mao</a>, <a href="/search/q-bio?searchtype=author&query=Vandewiele%2C+G">Gilles Vandewiele</a>, <a href="/search/q-bio?searchtype=author&query=Tinti%2C+M">Michele Tinti</a>, <a href="/search/q-bio?searchtype=author&query=Steenwinckel%2C+B">Bram Steenwinckel</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Takuya Ito</a>, <a href="/search/q-bio?searchtype=author&query=Noumi%2C+T">Taiga Noumi</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+S">Shujun He</a>, <a href="/search/q-bio?searchtype=author&query=Ishi%2C+K">Keiichiro Ishi</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+Y">Youhan Lee</a>, <a href="/search/q-bio?searchtype=author&query=%C3%96zt%C3%BCrk%2C+F">Fatih 脰zt眉rk</a>, <a href="/search/q-bio?searchtype=author&query=Chiu%2C+A">Anthony Chiu</a>, <a href="/search/q-bio?searchtype=author&query=%C3%96zt%C3%BCrk%2C+E">Emin 脰zt眉rk</a> , et al. (4 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.07531v2-abstract-short" style="display: inline;"> 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 ke… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.07531v2-abstract-full').style.display = 'inline'; document.getElementById('2110.07531v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.07531v2-abstract-full" style="display: none;"> 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 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.07531v2-abstract-full').style.display = 'none'; document.getElementById('2110.07531v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div 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