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    <responseDate>2026-10-15T12:41:01Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/7shf34snd3.2</identifier>
                <datestamp>2024-08-13T15:52:41Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Fan, Kunjie</dc:creator>
    <dc:title>Data for Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>Here stores the data we used to train and evaluate our MLEC-iSL model for individual sample-specific synthetic lethality prediction.

There are two types of features used in our model: omics features and biological networks. For omics features, there are two CCLE profiles (expression and essentiality) and four cell-specific features (expression, essentiality, mutation and copy number) considered in our model. As for network features, we incorporate physical protein-protein interaction (PPI) network, genetic interaction network and pathway network.</dc:description>
    <dc:subject>Systems Biology</dc:subject>
    <dc:subject>Computational Biology</dc:subject>
    <dc:subject>Deep Learning</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/7shf34snd3.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/7shf34snd3.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/7shf34snd3</dc:relation>
    <dc:date>2024-08-13T15:52:41Z</dc:date>
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