Poster Open Access

Interpretability for computational biology

Nguyen An-phi; Rodriguez-Martinez


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">computational biology</subfield>
  </datafield>
  <controlfield tag="005">20200120174543.0</controlfield>
  <controlfield tag="001">3374361</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">21-25 July 2019</subfield>
    <subfield code="g">ISMB/ECCB 2019</subfield>
    <subfield code="a">27th Conference on Intelligent Systems for Molecular Biology and the 18th European Conference on Computational Biology</subfield>
    <subfield code="c">Basel, Switzerland</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Rodriguez-Martinez</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">264381</subfield>
    <subfield code="z">md5:cbf9c8297795ce8862bf99ae4979e99c</subfield>
    <subfield code="u">https://zenodo.org/record/3374361/files/interpretability.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="y">Conference website</subfield>
    <subfield code="u">https://www.iscb.org/ismbeccb2019-program/tutorials</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2019-08-22</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-ipc</subfield>
    <subfield code="o">oai:zenodo.org:3374361</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Nguyen An-phi</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Interpretability for computational biology</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-ipc</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">826121</subfield>
    <subfield code="a">individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Why do we need interpretability to unveil the decision process ofa machine learning model?&lt;br&gt;
Trust - for high-risk scenarios, e.g. healthcare, the user needs to trust the decision taken.&lt;br&gt;
Debugging -&amp;nbsp;the model may be badly trained or there might be an unfair bias in either the dataset or the model itself.&lt;br&gt;
Hypothesis generation - surprising results might be consequences of new mechanisms or patterns unknown even to field experts.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3374360</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3374361</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">poster</subfield>
  </datafield>
</record>
175
185
views
downloads
All versions This version
Views 175175
Downloads 185185
Data volume 48.9 MB48.9 MB
Unique views 162162
Unique downloads 179179

Share

Cite as