Data Sheet 2_Comprehensive analysis of regulatory B Cell related genes in prognosis and therapeutic response in lung adenocarcinoma.xlsx حسب Liangyu Zhang (8947964)

"…Background<p>Regulatory B cells (Bregs) are a distinct subset of B cells that play a crucial role in regulating immune responses and maintaining immune tolerance in cancerous environments. …"

Data Sheet 1_Comprehensive analysis of regulatory B Cell related genes in prognosis and therapeutic response in lung adenocarcinoma.docx حسب Liangyu Zhang (8947964)

"…Background<p>Regulatory B cells (Bregs) are a distinct subset of B cells that play a crucial role in regulating immune responses and maintaining immune tolerance in cancerous environments. …"

Left: Quality profile of LAFM images for 1) periplasmic (top) and 2) cytoplasmic (bottom) SecYEG. حسب Creighton M. Lisowski (22756444)

Fig 7 - حسب Debao Yuan (4691992)

From timetable spaces to networks. حسب Fabio Lamanna (4961875)

A global view of gene ontology (GO) terms enriched in the set of gene pairs with gain-of-correlation (GOC) in resistant (RES) but not in susceptible (SUS) phenotypes. حسب Fang Liu (13271)

Composite statistics of modules inferred from resistant (RES) lambs that were preserved in susceptible (SUS) lambs. حسب Fang Liu (13271)

Schematic of the mean-field Ising simulation evolving in time. حسب Rostam M. Razban (22232522)

Table 1_ENO1-related gene signature predicts prognosis and therapeutic response in diffuse large B-cell lymphoma.docx حسب Wenli Yan (9235217)

"…Purpose<p>Alpha-enolase (ENO1), the enzyme catalyzing 2-phosphoglycerate conversion to phosphoenolpyruvate, is highly expressed in diffuse large B-cell lymphoma (DLBCL) and correlates with adverse clinical outcomes. …"

Performance comparison of NGNDG-AF, ReLU and ELU. حسب Ali Raza (3558965)

"…The efficacy of the optimized <i>CNN</i> method with <i>NGNDG-AF</i> was examined by a comparative study wirh popular <i>CNN</i> with various activation functions shows that better performance of <i>NGNDG-AF</i>, achieving the classification accuracy rates that are as high as <b>99<i>%</i></b> in training and <b>98<i>%</i></b> in the validation. …"

AUC of ResNet152V2 CNN with Adam for <i>β</i> = 1. حسب Ali Raza (3558965)

"…The efficacy of the optimized <i>CNN</i> method with <i>NGNDG-AF</i> was examined by a comparative study wirh popular <i>CNN</i> with various activation functions shows that better performance of <i>NGNDG-AF</i>, achieving the classification accuracy rates that are as high as <b>99<i>%</i></b> in training and <b>98<i>%</i></b> in the validation. …"

AUC of optimized CNN with Adam for <i>β</i> = 1. حسب Ali Raza (3558965)

"…The efficacy of the optimized <i>CNN</i> method with <i>NGNDG-AF</i> was examined by a comparative study wirh popular <i>CNN</i> with various activation functions shows that better performance of <i>NGNDG-AF</i>, achieving the classification accuracy rates that are as high as <b>99<i>%</i></b> in training and <b>98<i>%</i></b> in the validation. …"

Pt(100) surface slab + H, O, OH adsorbates structures for https://doi.org/10.1002/admi.202500680 حسب Renata Sechi (14553959)

"…</p><p dir="ltr">The structures are generated with a genetic algorithm.</p><p dir="ltr">Settings: dzp-LCAO basis set, PBE functional, 2x2x1 k-points grid. …"

The results of curve fitting analysis. حسب Hai-bo Ai (20790405)

Consensus clustering base on expression of cuproptosis genes. حسب Hongmin Luo (2142307)

Entry Point byte frequency حسب Paul A. Gagniuc (1818325)

Gradient Boosting Trees Machine Learning Approach to Predict the Mutation Types in CaSR. حسب Aylin Bircan (20176049)

Spherical cap variance for LP, PCS and MSP. حسب Luca Maria Del Bono (20479625)

GO/ KEGG enrichment analysis and correlation analysis of 11 ferroptosis-related genes. حسب Fang Zhang (197215)

Data Sheet 1_Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.pdf حسب Everest Castaneda (21758900)

"…Node and edge arrangements within gene networks impact comparisons between classes of disease, and connectivity metrics, such as those focused on degrees, betweenness, and centrality, do not yield sufficient discernment of disease network classification. …"