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        <datestamp>2026-09-30T17:52:10Z</datestamp>
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          <dc:title>&lt;p&gt;Gene Ontology (GO) functional enrichment analysis of predicted candidate effectors in Pseudomonas aeruginosa PAO1.&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;&lt;b&gt;A:&lt;/b&gt; The bar plot displays the number of genes shared among different combinations of Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), as indicated by the connected dot matrix below. &lt;b&gt;B:&lt;/b&gt; Ontology landscape. Overview of the significant GO terms and associated gene counts categorized by BP (blue), MF (teal), and CC (purple). &lt;b&gt;C:&lt;/b&gt; Representative enriched terms. Lollipop charts illustrating representative enriched terms for BP, MF, and CC, respectively. The x-axis represents the fold enrichment, and the bubble size and color intensity correspond to the statistical significance scaled as .&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T18:01:24Z</dc:date>
          <dc:type>Image</dc:type>
          <dc:type>Figure</dc:type>
          <dc:identifier>10.1371/journal.pcbi.1014344.g009</dc:identifier>
          <dc:relation>https://figshare.com/articles/figure/_p_Gene_Ontology_GO_functional_enrichment_analysis_of_predicted_candidate_effectors_in_Pseudomonas_aeruginosa_PAO1_p_/34036499</dc:relation>
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