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        <datestamp>2026-09-23T17:27:30Z</datestamp>
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          <dc:title>&lt;p&gt;(a) Evaluation of machine learning model performance on self-reported maternal family history prediction combing structural neuroimaging with mitochondrial haplogroups. (b) Variable importance analysis for top-performing structural neuroimaging and mitochondrial haplogroup models predicting positive self-reported maternal family history.&lt;/p&gt;</dc:title>
          <dc:creator>Olivia J. Veatch (9171779)</dc:creator>
          <dc:creator>Sreejata Dutta (22905308)</dc:creator>
          <dc:creator>Clayton O. Mansel (25099025)</dc:creator>
          <dc:creator>Ryan Townley (9986951)</dc:creator>
          <dc:creator>Mihaela E. Sardiu (259011)</dc:creator>
          <dc:creator>Robyn A. Honea (9704225)</dc:creator>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Cell Biology</dc:subject>
          <dc:subject>Neuroscience</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>Sociology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>vulnerable region linked</dc:subject>
          <dc:subject>using machine learning</dc:subject>
          <dc:subject>r0 intermediate clade</dc:subject>
          <dc:subject>na &amp;# 239</dc:subject>
          <dc:subject>identifying traits associated</dc:subject>
          <dc:subject>alzheimer &amp;# 8217</dc:subject>
          <dc:subject>important overall feature</dc:subject>
          <dc:subject>important feature correlated</dc:subject>
          <dc:subject>known family history</dc:subject>
          <dc:subject>family history information</dc:subject>
          <dc:subject>important mitochondrial haplogroup</dc:subject>
          <dc:subject>unknown family history</dc:subject>
          <dc:subject>reduced sample size</dc:subject>
          <dc:subject>mitochondrial haplogroups reduced</dc:subject>
          <dc:subject>disease &lt;/ p</dc:subject>
          <dc:subject>high ad risk</dc:subject>
          <dc:subject>family history</dc:subject>
          <dc:subject>sample size</dc:subject>
          <dc:subject>mitochondrial haplogroups</dc:subject>
          <dc:subject>feature importance</dc:subject>
          <dc:subject>often unknown</dc:subject>
          <dc:subject>mitochondrial genetics</dc:subject>
          <dc:subject>mitochondrial data</dc:subject>
          <dc:subject>risk stratification</dc:subject>
          <dc:subject>performed best</dc:subject>
          <dc:subject>origin effects</dc:subject>
          <dc:subject>normal cognition</dc:subject>
          <dc:subject>model optimization</dc:subject>
          <dc:subject>minimal impact</dc:subject>
          <dc:subject>informative variables</dc:subject>
          <dc:subject>imaging alone</dc:subject>
          <dc:subject>hyperparameter tuning</dc:subject>
          <dc:subject>fold cross</dc:subject>
          <dc:subject>findings reinforce</dc:subject>
          <dc:subject>complicating identification</dc:subject>
          <dc:subject>brain atrophy</dc:subject>
          <dc:subject>129 participants</dc:subject>
          <dc:description>&lt;p&gt;(a) Evaluation of machine learning model performance on self-reported maternal family history prediction combing structural neuroimaging with mitochondrial haplogroups. (b) Variable importance analysis for top-performing structural neuroimaging and mitochondrial haplogroup models predicting positive self-reported maternal family history.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-23T17:27:20Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.page.0000045.t005</dc:identifier>
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