This is raw data of IL-1β ELISA. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of IL-10 real-time PCR. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of Gal-9 real-time PCR. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of IL-10 ELISA. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of NF-κB real-time PCR. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of IL-1β real-time PCR. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of β-catenin real-time PCR. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

Details of the primers applied in real-time PCR. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

This is raw data of viability data. by Farzad Nasri (8941850)

“…These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.</p></div>…”

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”

Fig 5 - by Torsten Schober (20485754)

Fig 4 - by Torsten Schober (20485754)

Scatter plots of the linear regression for the relationship between the duration of the vegetative phase and the average mass of single inflorescences. by Torsten Schober (20485754)

Fig 6 - by Torsten Schober (20485754)

Relative expression of hub genes. by Ziyang Liu (242055)

PoAce1 enrichment in the specific regions of (hemi)cellulase genes assayed by ChIP-qPCR. by Zhongjiao Liu (11877464)

The effect of PoFbx23 on the protein abundance, transcription level, and DNA binding ability of PoAce1 under glucose signal. by Zhongjiao Liu (11877464)

Serum metabolomic response to aging. by Gwangho Yoon (5771678)

“…<b>(B)</b> Metabolites that decreased with aging. Statistical significance is indicated in the heatmap with asterisks. …”

Fig 9 - by Torsten Schober (20485754)

Reaction time and peak velocity across four cognitive-motor integration conditions during a youth soccer season across all athletes in the study. by Rachel S. Watson (21864061)