DataSheet_6_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.csv by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_1_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.pdf by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_5_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_7_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_4_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_3_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

Table1_The electrocardiographic, hemodynamic, echocardiographic, and biochemical evaluation of treatment with edaravone on acute cardiac toxicity of aluminum phosphide.XLSX by Nader Rahimi Kakavandi (13908558)

“…The rats were divided into six groups, including almond oil (control), normal saline, AlP (LD₅₀), and AlP + EDA (20, 30, and 45 mg/kg). …”

Rheological behavior of concentrated tucupi by Telma dos Santos COSTA (4618960)

“…Rheology at 25 °C indicated that the partial gelification of starch during concentration causes a decrease in the product’s viscosity and, if the concentration is carried out at a temperature that favors total starch gelification, the product’s viscosity increases. …”

Rheological behavior of concentrated tucupi by Telma dos Santos COSTA (4618960)

“…Rheology at 25 °C indicated that the partial gelification of starch during concentration causes a decrease in the product’s viscosity and, if the concentration is carried out at a temperature that favors total starch gelification, the product’s viscosity increases. …”

Table 6_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 3_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 1_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 4_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Data Sheet 1_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.docx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 5_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Simulated temporal dynamics in E2F activation using the stochastic Rb-E2F model. by Tae J. Lee (241477)

“…<p>(A) Stochastic simulations (25 events) exhibit variable time delays in E2F activation, as shown in gray lines. …”

DataSheet1_Non-target screening to track contaminant removal and release during nature-based water treatment.docx by Charlotte Guy (18392850)

“…Natural products predominantly increased after RB contact, while compounds exhibited a significant 75% decrease in peak area are mainly pharmaceuticals. …”

Table7_Non-target screening to track contaminant removal and release during nature-based water treatment.xlsx by Charlotte Guy (18392850)

“…Natural products predominantly increased after RB contact, while compounds exhibited a significant 75% decrease in peak area are mainly pharmaceuticals. …”

Table6_Non-target screening to track contaminant removal and release during nature-based water treatment.xlsx by Charlotte Guy (18392850)

“…Natural products predominantly increased after RB contact, while compounds exhibited a significant 75% decrease in peak area are mainly pharmaceuticals. …”

Low <i>VHL</i> mRNA Expression is Associated with More Aggressive Tumor Features of Papillary Thyroid Carcinoma by Boban Stanojevic (672033)