Predictive performance evaluation on real heart data with selected cell types. by Nuray Sogunmez Erdogan (21533872)

Spatial characterization of cancer-associated cell types in human breast cancer malignancies. by Nuray Sogunmez Erdogan (21533872)

Incremental Inverse Design of Desired Soybean Phenotypes by Joseph Zavorskas (19761296)

“…The limitations of inverse design in genotype-to-bulk phenotype (G-BP) mapping can be addressed via an established design paradigm: “design, build, test, learn” (DBTL), where computational inverse design automates both the design and learn phases. In any context, inverse design is limited by the fundamental “one-to-many” nature of the inverse function. …”

Table 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Table 2_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Table 3_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.docx by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Quantum Simulation of Molecular Dynamics ProcessesA Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware by Tamila Kuanysheva (21546962)

“…Although Qiskit provides a general method for initializing wave functions, in most cases it generates deep quantum circuits. …”

Identification and enrichment analysis of the TMGs. by Ravinder Sharma (13154079)

Hierarchical regression results. by Muna Saleh (10775575)

“…While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. …”

SHAP values (impact on model output). by Muna Saleh (10775575)

“…While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. …”

Variable importance for the fitted XGBoost model. by Muna Saleh (10775575)

“…While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. …”

Supplementary file 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.d... by Yuezhou Huang (9998177)

“…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …”

Supplementary Material for: A radiomics-based analysis of functional dopaminergic scintigraphic imaging for the diagnosis of dementia with Lewy bodies by figshare admin karger (2628495)

“…The xSPECT radiomics models showing the highest diagnostic performance were developed based on nine non-correlated features from both striatal regions and a support vector classifier (SVC) algorithm. …”

Table 11_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)

Table 6_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)

Table 15_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)

Table 9_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)

Table 8_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)

Table 10_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)

Table 7_Mitophagy-associated biomarkers and macrophage involvement in pulmonary arterial hypertension: identification and functional implications.xlsx by Xiaoyu Zhang (62901)