Data Sheet 1_Bioinformatics combined with machine learning unravels differences among environmental, seafood, and clinical isolates of Vibrio parahaemolyticus.pdf by Shuyi Feng (6315239)

“…Comparison of genomes of all seafood-clinical isolates showed high balanced accuracy (≥0.80) and Area Under the Receiver Operating Characteristics curve (≥0.87) for all of these functional features. Major virulence factors including tdh, trh, type III secretion system-related genes, and four alpha-hemolysin genes (hlyA, hlyB, hlyC, and hlyD) were identified as important differentiating factors in our seafood-clinical virulence model, underscoring the need for further investigation. …”

Data Sheet 1_Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer’s disease and glioblastoma.do... by Xuan Xu (781842)

“…Key hub genes, such as HS6ST3 and TUBB2B, were identified across different cellular subpopulations, highlighting a cell-specific co-expression network linked to mitochondrial function.…”

Table 1_Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer’s disease and glioblastoma.xlsx by Xuan Xu (781842)

“…Key hub genes, such as HS6ST3 and TUBB2B, were identified across different cellular subpopulations, highlighting a cell-specific co-expression network linked to mitochondrial function.…”

Table 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.xlsx by Liu Haoming (22524473)

“…</p>Conclusion<p>mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.…”

Data Sheet 2_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…</p>Conclusion<p>mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.…”

Data Sheet 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…</p>Conclusion<p>mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.…”

Transcription factor of Key regulator genes. by Kankana Bhattacharjee (20623639)

“…Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. …”

Gene-drug interaction of the key regulators. by Kankana Bhattacharjee (20623639)

“…Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. …”

Table 1_Developing models for the diagnosing of ulcerative colitis and prognosis of anti-TNF-α non-response based on neutrophil extracellular trap-associated genes.docx by Jinyuan Ou (22163431)

“…A diagnostic model for UC was constructed using five hub genes (FCGR3B, IL1RN, CXCL8, S100A8, and S100A9) derived from C1. …”

Risk of Bias 2 Assessment: BoNT-A versus Placebo by Anita R Gross (9363502)

“…<p dir="ltr">The Risk of Bias 2 (RoB 2) tool was employed to assess the methodological quality of included studies for botulinum toxin A versus placebo. …”

Table 1_Identification of cellular senescence-related genes as biomarkers for lupus nephritis based on bioinformatics.docx by Wei Chen (23863)

“…The three biomarkers were enriched in “B Cell receptor signaling pathway” and “NF−kappa B signaling pathway” based on GESA results.…”

Image 1_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…</p>Results<p>TRLW-2 exhibited high affinity for pocket 1-5, forming key hydrogen bonds with residues including Ser350, Asp352, Asp363, Thr398, and Arg401, which was validated by molecular dynamics simulations (MD) and site-directed mutagenesis. Functionally, TRLW-2 acted as a potent PXR agonist, significantly upregulating detoxifying enzymes (such as Cyp2b10, Cyp3a11 and Ugt1a1) and proliferation markers (PCNA, CDK1, Cyclin B1, and Ki67) in vitro. …”

Image 2_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…</p>Results<p>TRLW-2 exhibited high affinity for pocket 1-5, forming key hydrogen bonds with residues including Ser350, Asp352, Asp363, Thr398, and Arg401, which was validated by molecular dynamics simulations (MD) and site-directed mutagenesis. Functionally, TRLW-2 acted as a potent PXR agonist, significantly upregulating detoxifying enzymes (such as Cyp2b10, Cyp3a11 and Ugt1a1) and proliferation markers (PCNA, CDK1, Cyclin B1, and Ki67) in vitro. …”

Image 3_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…</p>Results<p>TRLW-2 exhibited high affinity for pocket 1-5, forming key hydrogen bonds with residues including Ser350, Asp352, Asp363, Thr398, and Arg401, which was validated by molecular dynamics simulations (MD) and site-directed mutagenesis. Functionally, TRLW-2 acted as a potent PXR agonist, significantly upregulating detoxifying enzymes (such as Cyp2b10, Cyp3a11 and Ugt1a1) and proliferation markers (PCNA, CDK1, Cyclin B1, and Ki67) in vitro. …”

Image 4_Targeting a distinct binding pocket in the pregnane X receptor with natural agonist TRLW-2 ameliorates murine ulcerative colitis.tif by Shangrui Rao (18189241)

“…</p>Results<p>TRLW-2 exhibited high affinity for pocket 1-5, forming key hydrogen bonds with residues including Ser350, Asp352, Asp363, Thr398, and Arg401, which was validated by molecular dynamics simulations (MD) and site-directed mutagenesis. Functionally, TRLW-2 acted as a potent PXR agonist, significantly upregulating detoxifying enzymes (such as Cyp2b10, Cyp3a11 and Ugt1a1) and proliferation markers (PCNA, CDK1, Cyclin B1, and Ki67) in vitro. …”

Image 2_Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.jpeg by Ting Chen (15205)

“…</p>Results<p>The improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”

Image 1_Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.tif by Shoupeng Ding (21555394)

“…Consensus clustering based on the expression profiles of these core genes stratified patients into two distinct subtypes. Functional enrichment analysis further underscored the predominance of immune-related pathways in subtype B. …”

Image 3_Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.jpeg by Ting Chen (15205)

“…</p>Results<p>The improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”

Image 1_Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.jpeg by Ting Chen (15205)

“…</p>Results<p>The improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”

Data Sheet 2_Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.pdf by Shoupeng Ding (21555394)

“…Consensus clustering based on the expression profiles of these core genes stratified patients into two distinct subtypes. Functional enrichment analysis further underscored the predominance of immune-related pathways in subtype B. …”