Comparative analysis of WordNet replacement impact on data distribution. حسب Enock Niyonkuru (21246879)

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery حسب M RN (9866504)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection حسب Samin Aref (4683934)

"…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…"

Flow Φ of the controlled continuous time PDMP. حسب Benoîte de Saporta (20454662)

"…We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phases, and control the model via long-term cost function optimization. This considers treatment side effects, visit burden, and quality of life, using noisy blood marker measurements for feedback. …"

Schematic representation of the placenta and feto-placental vascular structures. حسب Diana C. de Oliveira (9220729)

Schematic representation of important placental morphological parameters. حسب Diana C. de Oliveira (9220729)

Residual risk of undetected blood-borne virus infection in deceased organ donors: Residual risk visualiser website حسب Martin Dutch (6601742)

"…</p><p dir="ltr">These include:</p><ul><li>Assay's X50 Limit of Detection (cp/ml)<br>Assay's X95 Limit of Detection (cp/ml)</li><li>Viral Doubling Rate (days)</li><li>The minimum infective concentration (cp/mL)</li><li>Incidence Rate of new infections (100 PY)</li></ul><h3>Model Outputs</h3><p dir="ltr">The Visualiser produces the following outputs</p><ul><li>A graph of the probability of non-detection after exposure as a function of time prior to blood draw.</li><li>An estimation of the assay's un-adjusted window period. …"

Construction of non-invasive diagnostic model and nomogram of AR. حسب Xihao Shen (20347942)

The Characteristics of Neutrophil Groups in the Control and PCAS Groups Are Illustrated by the Dimension Reduction Map. حسب Yuki Chiba (18709038)

A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. حسب Jamison H. Burks (21029607)

Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip حسب Rui Zhang (13940)

"…</p>Results<p>Multivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …"

Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx حسب Rui Zhang (13940)

"…</p>Results<p>Multivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …"

Image 2_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.tif حسب Qi Wu (263110)

"…Differentially expressed genes (DEGs) were screened using the limma package. Functional enrichment analysis was performed, and a PPI network was constructed. …"

Table 1_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.xlsx حسب Qi Wu (263110)

"…Differentially expressed genes (DEGs) were screened using the limma package. Functional enrichment analysis was performed, and a PPI network was constructed. …"

Image 3_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.tif حسب Qi Wu (263110)

"…Differentially expressed genes (DEGs) were screened using the limma package. Functional enrichment analysis was performed, and a PPI network was constructed. …"

Image 4_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.tif حسب Qi Wu (263110)

"…Differentially expressed genes (DEGs) were screened using the limma package. Functional enrichment analysis was performed, and a PPI network was constructed. …"

Image 1_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.jpeg حسب Qi Wu (263110)

"…Differentially expressed genes (DEGs) were screened using the limma package. Functional enrichment analysis was performed, and a PPI network was constructed. …"

Clinical Characteristics of Patients. حسب Yifei Wang (95207)

"…<div><p>Background</p><p>The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. …"

Patient screening information for this study. حسب Yifei Wang (95207)

"…<div><p>Background</p><p>The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. …"

Data Sheet 1_Genome-wide expression in human whole blood for diagnosis of latent tuberculosis infection: a multicohort research.pdf حسب Fan Jiang (144211)

"…Cohorts were stratified into training (8 cohorts, n = 1,933) and validation sets (3 cohorts, n = 825) based on functional assignment.</p>Results<p>Through Upset analysis, LASSO (Least Absolute Shrinkage and Selection Operator), SVM-RFE (Support Vector Machine Recursive Feature Elimination), and MCL (Markov Cluster Algorithm) clustering of protein–protein interaction networks, we identified S100A12 and S100A8 as optimal biomarkers. …"