Characterization of anesthetic effects on network dynamics and their simulated ACh reversal for best fit. حسب Bolaji P. Eniwaye (12915339)

DataSheet1_Equivalent of distribution network with distributed photovoltaics for electromechanical transient study based on user-defined modeling.ZIP حسب Zhe Jiang (6903)

"…Finally, the particle swarm optimization (PSO) algorithm is used to obtain the parameters of the equivalent PV. …"

DataSheet1_Equivalent of distribution network with distributed photovoltaics for electromechanical transient study based on user-defined modeling.ZIP حسب Zhe Jiang (6903)

"…Finally, the particle swarm optimization (PSO) algorithm is used to obtain the parameters of the equivalent PV. …"

MOA classification performance and model benchmarking. حسب Josh L. Espinoza (10492448)

"…B) The influence of the <i>Clairvoyance</i> optimization algorithm for feature selection on model performance at each of the 5 sub-model decision points. …"

Results for the Pattern Generation task. حسب Paolo Muratore (8331585)

Probability flux balances can determine biochemical rates regardless of global network dynamics. حسب Timon Wittenstein (12908474)

"…Although probability distributions differed greatly between the four systems, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010183#pcbi.1010183.e006" target="_blank">Eq (4)</a> could identify the functional dependence of the production rate of X₃ based on the numerical convex optimization algorithm detailed in the Materials & Methods. …"

S1 Fig - حسب Aniket Ravan (3174171)

Flowchart of software workflow. حسب Gavrielle R. Untracht (11820311)

Breast cancer specific subnetwork. حسب Bengi Ruken Yavuz (21983004)

Methodology block diagram. حسب Gahao Chen (21688843)

"…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …"

Untitled Item حسب Caio Vieira Arasaki (15508650)

<b>Road intersections Data with branch information extracted from OSM</b> & <b>C</b><b>odes to implement the extraction </b>&<b> I</b><b>nstructions on how to </b><b>reproduce each... حسب Zihao Tang (19794537)

"…</p><p dir="ltr"><b>This paper proposes a method for identifying intersections based on OpenStreetMap data, which records networks at the lane level.…"

Table 3_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx حسب Jian Wang (5901)

"…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …"

Table 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx حسب Jian Wang (5901)

"…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …"

Image 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.tif حسب Jian Wang (5901)

"…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …"

Table 2_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx حسب Jian Wang (5901)

"…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …"

DataSheet_1_Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, an... حسب Fang Zhang (197215)

"…In addition, weighted gene coexpression network analysis was used to construct an lncRNA–miRNA–mRNA competitive endogenous network. …"

BOFdat Step 3: Identifying species-specific metabolic end goals. حسب Jean-Christophe Lachance (6619307)

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"