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Erva Ulusoy
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1d133b0
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Parent(s):
0d1479e
changed workflow pdf to png it was not working
Browse files- figures/ProtHGT_workflow.pdf +0 -0
- figures/ProtHGT_workflow.png +0 -0
- pages/About.py +1 -4
figures/ProtHGT_workflow.pdf
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Binary file (467 kB)
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figures/ProtHGT_workflow.png
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pages/About.py
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@@ -37,10 +37,7 @@ Overall workflow of ProtHGT is shown below.
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""")
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st.subheader('Schematic overview of ProtHGT', anchor='schematic-overview')
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base64_pdf = base64.b64encode(pdf_file.read()).decode('utf-8')
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pdf_display = F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="750" height="550" type="application/pdf"></iframe>'
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st.markdown(pdf_display, unsafe_allow_html=True)
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st.markdown(
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'<p style="text-align:center"><em><strong>Schematic representation of the ProtHGT framework. a)</strong> Diverse biological datasets, including proteins, pathways, domains, and GO terms, are integrated into a unified knowledge graph; <strong>b)</strong> the heterogeneous graph is constructed, capturing multi-relational biological associations; <strong>c)</strong> feature vectors for each node type are generated using state-of-the-art embedding methods; <strong>d)</strong> protein function prediction models are trained separately for molecular function, biological process, and cellular component sub-ontologies; <strong>e)</strong> heterogeneous graph transformer (HGT) layers process and refine node representations through multi-relational message passing. Final protein function predictions are obtained by linking proteins to GO terms based on learned embeddings and attention-weighted relationships.</em></p>', unsafe_allow_html=True)
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""")
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st.subheader('Schematic overview of ProtHGT', anchor='schematic-overview')
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st.image('figures/ProtHGT_workflow.png')
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st.markdown(
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'<p style="text-align:center"><em><strong>Schematic representation of the ProtHGT framework. a)</strong> Diverse biological datasets, including proteins, pathways, domains, and GO terms, are integrated into a unified knowledge graph; <strong>b)</strong> the heterogeneous graph is constructed, capturing multi-relational biological associations; <strong>c)</strong> feature vectors for each node type are generated using state-of-the-art embedding methods; <strong>d)</strong> protein function prediction models are trained separately for molecular function, biological process, and cellular component sub-ontologies; <strong>e)</strong> heterogeneous graph transformer (HGT) layers process and refine node representations through multi-relational message passing. Final protein function predictions are obtained by linking proteins to GO terms based on learned embeddings and attention-weighted relationships.</em></p>', unsafe_allow_html=True)
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