Visualization by PaCMAP [52] shows that persistence fingerprint clusters protein-ligand complexes with similar binding affinity reasonably well, even beyond the training dataset (P... by Yuxi Long (11024307)

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Profile of dysregulated proteins in SCA1 cells containing polyQ inclusions. by Aimilia-Christina Vagiona (17584758)

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Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<b>(B)</b> The priority of the top 10 genes was evaluated through MNC, which identifies clusters of protein nodes that are more functionally connected to each other and selects the central proteins within the cluster. …”

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

Data Sheet 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.docx by Jingjing Chen (293564)

“…</p>Methods<p>We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. …”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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A protein-protein interaction (PPI) map of the paclitaxel associated DRG proteome. by Rania Hanna (18830411)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Key Genes Screening and LASSO Risk Model Construction. by Jianguo Ma (2119684)