Dataset for Partial Parallelism Plot Analysis in Neurodegeneration Biomarker Assays (2010–2024) by Axel Petzold (7076261)

“…<br></p><p dir="ltr">Each dataset entry is annotated with:</p><ul><li>Sample type (serum, plasma, cerebrospinal fluid)</li><li>Assay platform and dilution steps</li><li>Classification of outcome (partial parallelism achieved or not)</li></ul><p dir="ltr"><b>Use cases:</b><br>This dataset is designed to help researchers, assay developers, and meta-analysts to:</p><ul><li>Reproduce figures and analyses from the published review</li><li>Benchmark or validate new assay performance pipelines</li><li>Train algorithms for automated detection of dilutional non-parallelism</li></ul><p dir="ltr"><b>Files included:</b></p><ul><li><code>.csv</code> files containing dilution–response data</li><li>Metadata spreadsheets with assay and sample annotations</li></ul><p></p>…”

archive.zip by Tasneem Fatima (21976946)

“…The structured dataset suggests an intent to train and evaluate deep learning algorithms on real-world image data for practical deployment in grain procurement or quality monitoring systems.…”

Massive Mixed Models in Julia by Phillip M. Alday (2814652)

“…In contrast, an approach based on penalized least squares can take advantage of sparse matrix methods to scale to models with millions of observations and handles nesting and crossing of random effects in a general way. …”

Table 1_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf by Salwa Hassanein (20843468)

“…Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”

Table 2_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf by Salwa Hassanein (20843468)

“…Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”

Table 3_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf by Salwa Hassanein (20843468)

“…Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”

Table 5_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.xlsx by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Image 3_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Data Sheet 2_Impact of climate change on the potential global prevalence of Macrophomina phaseolina (Tassi) Goid. under several climatological scenarios.csv by Peter F. Farag (21097466)

“…Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.…”

Data Sheet 1_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.docx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Image 1_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Data Sheet 6_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv by Ge Jin (347352)

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”

Image 2_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.tif by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

DataSheet1_Identification of potential auxin response candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis.zip by Deisiany Ferreira Neres (10484791)

“…Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops.…”

Data Sheet 1_A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema.docx by Wenrui Lu (5526404)

“…Five machine learning algorithms—logistic regression, decision tree, multilayer perceptron, random forest, and support vector machine—were trained and validated. …”

Video 4_Paired reentries maintain ventricular tachycardia: a topological analysis of arrhythmic mechanisms using the index theorem.mp4 by Robin Van Den Abeele (16664361)

“…This ablation strategy consistently terminated all simulations, supporting the applicability of our topology-based approach to VT.</p>Conclusion<p>The index theorem remains valid for scar-related VT. …”

Data Sheet 8_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”

Data Sheet 1_Impact of climate change on the potential global prevalence of Macrophomina phaseolina (Tassi) Goid. under several climatological scenarios.zip by Peter F. Farag (21097466)

“…Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.…”

Table 1_Multimodal integration of [18F]PSMA-1007 PET/CT semiquantitative parameters and clinicopathological data for predicting prostate cancer metastasis.docx by JiaYing Yang (22434829)

“…</p>Results<p>Among the five algorithms, the XGBoost model achieved an accuracy of 90.32%, sensitivity of 90.0%, specificity of 94.74%, and an AUC of 0.8977. …”