Table 1_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

“…Inflammatory-related COPD feature genes were selected using Lasso regression and random forest algorithms, and a COPD risk prediction model was constructed. …”

Image 3_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.tif by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Image 2_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.tif by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Table 4_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.csv by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Table 1_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.csv by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Table 5_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.csv by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Table 3_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.xlsx by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Table 2_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.xlsx by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Image 1_Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.tif by Mengyao Sha (15775124)

“…Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). …”

Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…To solve the optimization problem, a semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. Numerical investigations demonstrate and validate the proposed method.…”

Structure of optimized model parameters in the high-dimensional cases. by Kevin J. Wischnewski (21354521)

“…The number and size of the clusters were determined with help of the -means clustering method. Both were set to zero if the absolute mean value of the off-diagonal elements in the correlation matrix (cf. …”

Multi-Task Learning in Analyzing the Working capacity of MOFs by Junhui Kou (20327073)

“…</p><ul><li><b>CIF files</b>: CIF files for 252,352 MOFs;</li><li><b>Geometric descriptors</b>: 14 geometric descriptors;</li><li><b>Chemical descriptors</b>: 176 chemical descriptors;</li><li><b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);</li><li><b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;</li><li><b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.…”

Image 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.tif by Can Qi (540350)

“…A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. …”

Table 3_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. …”

Table 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. …”

Table 2_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. …”

Supplementary file 1_SLC11A1 protein as a key regulator of iron metabolism, ferroptosis mediator, and putative therapeutic target in nonalcoholic fatty liver disease: an integrated... by Yang Wang (5921)

“…Key regulatory proteins—ERN1, SLC11A1, MYC, TLR7, and PPARGC1A—were screened using weighted gene co-expression network analysis (WGCNA) and a machine learning algorithm (LASSO). Their correlations with immune microenvironment features were also evaluated. …”

Supplementary file 1_An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction.docx by Songpeng Zhao (21563714)

“…Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. …”

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

“…</b></p><p dir="ltr">*Correspondence: Klaus Linkenkaer-Hansen (klaus.linkenkaer@cncr.vu.nl)</p><p dir="ltr"><br></p><p dir="ltr">In this study, we investigated fE/I, θ-γ PAC, and epileptiform features in hippocampal and cortical local field potentials (LFPs) recorded weekly in freely behaving male APPswe/PS1dE9 (APP/PS1) mice (<i>n</i> = 10) and wildtype controls (<i>n</i> = 10) between 3 and up to and including 11 months of age.</p><p dir="ltr">All data are available upon request. The standalone Python implementation of the fE/I algorithm is available under a CC-BY-NC-SA license at <a href="https://github.com/arthur-ervin/crosci" target="_blank">https://github.com/arthur-ervin/crosci</a>. …”

<b>An Empirical Evaluation of Software Quality Classification Based on User Feedback Aligned</b><b>with ISO/IEC 25010</b> by mesut polatgil (22408147)

“…<p dir="ltr">Evaluating software quality without access to the source code is a challenging task, as traditional metrics and testing approaches often rely on internal code analysis. …”