IMU data and video synchronization. by Saravanan Manoharan (21273304)

“…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …”

Confusion matrix-punch classification. by Saravanan Manoharan (21273304)

“…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …”

Experimental design of this study. by Renya Kawakami (20469088)

“…In fact, naive old males exhibited significantly higher paternity success compared with old males who had previously mated. …”

All relevant data of this study. by Renya Kawakami (20469088)

“…In fact, naive old males exhibited significantly higher paternity success compared with old males who had previously mated. …”

Effect of the replacement parameter . by Fynn Bachmann (21409831)

F05 demonstrates additive anti-tumor effects in combination with mAb therapy and reprograms M2 macrophages towards a phagocytotic phenotype. by Keifer G. Kurtz (21416918)

ASP protects the AC16 cells from DOX-induced cardiotoxicity via Nrf2 activation. by Xueyang Bai (20550422)

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 3 - by Micha Keller (11312748)

“…<p>Estimated marginal means (± SE) of momentary frequency of highest amplitude (MFHA; A.1-A.3) as well as amplitudes (B.1-B.3) for each intervention, LF, IM, and HF frequency bands, and each section. Due to non-significant main effect of ‘time’, means across measurement days are plotted. …”

Main results and moderating effects. by Hao Wang (39217)

“…This study employs fixed-effects models for a panel data. The findings reveal that minimum wage increases are significantly associated with a reduction in both strategic CSR and responsive CSR. …”

Sequential viral infection modulates the intestinal bacterial microbiome. by Yuhao Li (1559569)

The model in which the PoFbx23 is required in activating PoAce1 to control the expression of target genes. by Zhongjiao Liu (11877464)

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Data that underlies the TAP-MS of three Ace1-TAP samples. by Zhongjiao Liu (11877464)

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Data that underlies the ChIP-qPCR in this paper. by Zhongjiao Liu (11877464)

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The results of TAP-MS using transcription factor PoAce1 as bait. by Zhongjiao Liu (11877464)

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The top proteins interacting with PoAce1 identified through TAP-MS. by Zhongjiao Liu (11877464)

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The result of TAP-MS using PoAce1 as the bait. by Zhongjiao Liu (11877464)

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The interaction between PoFbx23A and PoAce1. by Zhongjiao Liu (11877464)

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The phenotype of the Δ<i>ace1</i> and (hemi)cellulase production of various mutants. by Zhongjiao Liu (11877464)

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Subcellular localization of PoFbx23A and PoAce1. by Zhongjiao Liu (11877464)

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