Details of 23 basic benchmark functions. by Ying Li (38224)

Heatmap of control parameters from CoCaBO. by Xianglong Tan (2818864)

Growth rate comparison between models and experimental results in mono-culture and co-culture. by Elias Vera-Siguenza (16993830)

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

“…<p>A) The empirically determined structure of the CoHEC model calibrated to predict the MOA of an unobserved antibacterial compound based on the transcriptional change profiles of <i>E</i>. …”

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

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 by 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 by 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 by 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 by 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. …”

Neuron tracking and mapping of neuronal ensembles in behaving Hydra. by Thibault Lagache (492571)

“…Peaks of activity (highlighted with red stars) correspond to significant co-activity of individual neurons (sum of individual activities (solid red line) > statistical threshold (dashed red line), p = 0.001 see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1009432#sec013" target="_blank">Materials and Methods</a>). …”

The Geography of Oxia Planum 03 CTX DEM Mosaic by Peter Fawdon (10016036)

“…</div><div><b><br></b></div><div><b>Georeferencing</b></div><div>Mars Express High Resolution Stereo Camera (HRSC; Gwinner et al., 2016) MC11- mosaic (Kersten et al., 2018) has been used as the base control mosaic (tile HMC_11W24_co5ps.tif http://hrscteam.dlr.de/HMC30/).. …”