Poster Open Access

A learned embedding for efficient joint analysis of millions of mass spectra

Bittremieux, Wout; May, Damon H.; Bilmes, Jeffrey; Noble, William Stafford


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3576516</identifier>
  <creators>
    <creator>
      <creatorName>Bittremieux, Wout</creatorName>
      <givenName>Wout</givenName>
      <familyName>Bittremieux</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3105-1359</nameIdentifier>
      <affiliation>University of California San Diego, La Jolla, CA, USA</affiliation>
    </creator>
    <creator>
      <creatorName>May, Damon H.</creatorName>
      <givenName>Damon H.</givenName>
      <familyName>May</familyName>
      <affiliation>University of Washington, Seattle, WA, USA</affiliation>
    </creator>
    <creator>
      <creatorName>Bilmes, Jeffrey</creatorName>
      <givenName>Jeffrey</givenName>
      <familyName>Bilmes</familyName>
      <affiliation>University of Washington, Seattle, WA, USA</affiliation>
    </creator>
    <creator>
      <creatorName>Noble, William Stafford</creatorName>
      <givenName>William Stafford</givenName>
      <familyName>Noble</familyName>
      <affiliation>University of Washington, Seattle, WA, USA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A learned embedding for efficient joint analysis of millions of mass  spectra</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-12-14</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Poster</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3576516</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3572596</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;We propose to train a Siamese neural network using peptide&amp;ndash;spectrum assignments to embed spectra in a low-dimensional space such that spectra generated by the same peptide are close to one another. We demonstrate that this learned embedding captures latent properties of the mass spectra, clusters related spectra in the low-dimensional space, and identifies the &amp;quot;dark matter&amp;quot; of the human proteome.&lt;/p&gt;</description>
  </descriptions>
</resource>
168
249
views
downloads
All versions This version
Views 16865
Downloads 24964
Data volume 322.7 MB82.9 MB
Unique views 14760
Unique downloads 22862

Share

Cite as