MAE versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³. حسب Limin Ma (556873)

Evaluation metrics graphs for different loss functions. حسب Huiying Zhang (200681)

Testing function diagram of QIMPN-PCA-DC. حسب Jun Zhao (59250)

Boxplots showcasing variability of additional key topological metrics obtained after 50 algorithm runs for the generation of a) chorionic vessels and b) villous vessels. حسب Diana C. de Oliveira (9220729)

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. حسب Yuxi Long (11024307)

"…The benchmarked algorithms include physics-based and deep learning algorithms from the famous AutoDock framework (scoring function of AutoDock4 implemented in the AutoDockFR package [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref068" target="_blank">68</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref077" target="_blank">77</a>], Vinardo [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref069" target="_blank">69</a>], GNINA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref070" target="_blank">70</a>]), empirical (AA-Score [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref071" target="_blank">71</a>]), knowledge-based (SMoG2016 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref072" target="_blank">72</a>]), and deep learning-based scoring functions (OnionNet [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref073" target="_blank">73</a>], PLANET [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref074" target="_blank">74</a>]). …"

Risk and density functions for standard relapse from remission condition to disease <i>b</i> condition (similar shapes for standard relapse to disease <i>a</i>). حسب Benoîte de Saporta (20454662)

Perturbations and their effects within networks. حسب Matthew Aguirre (9558032)

"…<b>(D)</b> KO effects as a function of network distance between two genes, and <b>(E)</b> within and across modules given by the generating algorithm. …"

Cell2Spatial enables versatile applications across various analytical domains. حسب Huamei Li (8815955)

Investigating the spatial architecture of the mouse kidney. حسب Huamei Li (8815955)

Performance assessment of Cell2Spatial in real ST data from various tissue types. حسب Huamei Li (8815955)

Hash function construct used in SKINNY-tk3-hash. حسب Sonal Arvind Barge (20454967)

"…IoT devices are secured by cryptographic algorithms, but their dependability is a major concern. …"

Performance assessment of Cell2Spatial on real and simulated mouse brain spatial transcriptomics (ST) data. حسب Huamei Li (8815955)

Functional enrichment analysis. حسب Junjie Su (368807)

Exploring the spatial architecture of the human dorsolateral prefrontal cortex (DPLFC). حسب Huamei Li (8815955)

Classification accuracy and information transfer rate (ITR) as a function of the measurement set-up and the number of combined trials. حسب Dovilė Kurmanavičiūtė (21072549)

Application of Cell2Spatial to 10× Genomics Xenium mouse brain data. حسب Huamei Li (8815955)

Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions. حسب Huamei Li (8815955)

Membership function of output for tank 1. حسب Tamiru Demelash Kassie (20678515)

Membership function of output for tank 2. حسب Tamiru Demelash Kassie (20678515)

RTC-F solar cell: (a) <i>I</i>-<i>U</i> and (b) <i>P</i>-<i>U</i> characteristics. حسب Ziad M. Ali (12516532)