Alpha networks. حسب Ying Joey Zhou (22479044)

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Data Sheet 1_Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review.pdf حسب Catalina Gomez (7057646)

"…<p>Leveraging Artificial Intelligence (AI) in decision support systems has disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. …"

Schematic diagram of proposed model averaging method. حسب GaoXiang Zhao (21499525)

Internal validation performance metrics. حسب Xie Qiu (13243770)

"…</p><p>Results</p><p>The IBSLO demonstrated significantly superior convergence performance versus original BSLO, ant lion optimizer (ALO), Harris hawks optimization (HHO), and whale optimization algorithm (WOA) across all 12 CEC2022 test functions. …"

Training Set Performance Evaluation. حسب Xie Qiu (13243770)

"…</p><p>Results</p><p>The IBSLO demonstrated significantly superior convergence performance versus original BSLO, ant lion optimizer (ALO), Harris hawks optimization (HHO), and whale optimization algorithm (WOA) across all 12 CEC2022 test functions. …"

Testing Set Classification Performance. حسب Xie Qiu (13243770)

"…</p><p>Results</p><p>The IBSLO demonstrated significantly superior convergence performance versus original BSLO, ant lion optimizer (ALO), Harris hawks optimization (HHO), and whale optimization algorithm (WOA) across all 12 CEC2022 test functions. …"

Screening process for marker genes from 7 DEGs and the diagnostic value of marker genes. حسب Kaiyi Zhou (2553352)

The information of datasets used in this study. حسب Kaiyi Zhou (2553352)

"…</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). …"

The workflow of the present study. حسب Kaiyi Zhou (2553352)

"…</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). …"

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)

Diagrammatic sketch of railway grid. حسب Liying Wang (288626)

"…These expressions are determined using the copula function, and an algorithm is designed to construct the corresponding transition probability matrix. …"

Parameters of the Gamma increment processes. حسب Liying Wang (288626)

"…These expressions are determined using the copula function, and an algorithm is designed to construct the corresponding transition probability matrix. …"

Cost parameters. حسب Liying Wang (288626)

"…These expressions are determined using the copula function, and an algorithm is designed to construct the corresponding transition probability matrix. …"