<?xml version='1.0' encoding='utf-8'?>
<?xml-stylesheet type="text/xsl" href="/v2/static/oai2.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-10-10T16:26:42Z</responseDate>
  <request identifier="oai:figshare.com:article/34036514" metadataPrefix="oai_dc" verb="GetRecord">https://api.figshare.com/v2/oai</request>
  <GetRecord>
    <record>
      <header>
        <identifier>oai:figshare.com:article/34036514</identifier>
        <datestamp>2026-09-30T17:52:16Z</datestamp>
        <setSpec>category_1</setSpec>
        <setSpec>category_4</setSpec>
        <setSpec>category_8</setSpec>
        <setSpec>category_21</setSpec>
        <setSpec>category_734</setSpec>
        <setSpec>category_931</setSpec>
        <setSpec>category_132</setSpec>
        <setSpec>portal_5</setSpec>
        <setSpec>item_type_3</setSpec>
        <setSpec>month_year_09_2026</setSpec>
      </header>
      <metadata>
        <oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"  xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>&lt;p&gt;Performance evaluation metrics for different fusion strategies.&lt;/p&gt;</dc:title>
          <dc:creator>Shouzhen Song (25145501)</dc:creator>
          <dc:creator>Hua Shi (7282)</dc:creator>
          <dc:creator>Hongfeng Wu (10227425)</dc:creator>
          <dc:creator>Dachen Liu (23461465)</dc:creator>
          <dc:creator>Yihang Lin (10198502)</dc:creator>
          <dc:creator>Nor Ashidi Mat Isa (25145504)</dc:creator>
          <dc:creator>Quan Zou (157931)</dc:creator>
          <dc:creator>Leyi Wei (3959444)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Microbiology</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Infectious Diseases</dc:subject>
          <dc:subject>t6se prediction tasks</dc:subject>
          <dc:subject>substantial application potential</dc:subject>
          <dc:subject>spatial conformational patterns</dc:subject>
          <dc:subject>maintaining stable performance</dc:subject>
          <dc:subject>local spatial topology</dc:subject>
          <dc:subject>linear functional motifs</dc:subject>
          <dc:subject>grained token level</dc:subject>
          <dc:subject>developing precise anti</dc:subject>
          <dc:subject>module enhances consistency</dc:subject>
          <dc:subject>existing leading models</dc:subject>
          <dc:subject>effector proteins secreted</dc:subject>
          <dc:subject>although existing methods</dc:subject>
          <dc:subject>model integrates sequence</dc:subject>
          <dc:subject>enable effective cross</dc:subject>
          <dc:subject>models associations</dc:subject>
          <dc:subject>effector functions</dc:subject>
          <dc:subject>based methods</dc:subject>
          <dc:subject>attention module</dc:subject>
          <dc:subject>virulence functions</dc:subject>
          <dc:subject>tokenized self</dc:subject>
          <dc:subject>synergistic modeling</dc:subject>
          <dc:subject>residue orientations</dc:subject>
          <dc:subject>protein sequence</dc:subject>
          <dc:subject>negative bacteria</dc:subject>
          <dc:subject>modal fusion</dc:subject>
          <dc:subject>modal alignment</dc:subject>
          <dc:subject>infective strategies</dc:subject>
          <dc:subject>geometric spaces</dc:subject>
          <dc:subject>fully capture</dc:subject>
          <dc:subject>extensive evaluations</dc:subject>
          <dc:subject>extensible architecture</dc:subject>
          <dc:subject>contrastive learning</dc:subject>
          <dc:subject>contextual embeddings</dc:subject>
          <dc:description>&lt;p&gt;Performance evaluation metrics for different fusion strategies.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T18:01:24Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pcbi.1014344.t004</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/_p_Performance_evaluation_metrics_for_different_fusion_strategies_p_/34036514</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
        </oai_dc:dc>
      </metadata>
    </record>
  </GetRecord>
</OAI-PMH>
