Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates by Peng Gao (35465)

Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates by Peng Gao (35465)

Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Subjects:

Venn diagrams showing overlap of the total of overrepresented/underrepresented kinases for both cohorts. by Erica A. K. DePasquale (11851483)

The image of the Aekley function. by Zhengyu Xu (8550660)

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides by Bo-Siyuan Jian (14669996)

Liquid character of phases. by Simon M. Lichtinger (11326846)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-01. by Tingting Liu (267387)

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

The IALO algorithm solution flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”

Usefulness of template-based module and merging module. by Zheng Jiang (282949)

Comparison of the performance of both models against 13 other models on the independent test dataset. by Aastha Vatsyayan (9981916)

Varietal predicted parameters under both water conditions per groups of varieties. by Maud Delattre (19106551)

Proposed architecture testing phase. by Yasir Khan Jadoon (21433231)

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. …”

Evolution of the rescaled hydraulic diameter () and complexity () in both simulations and seal tomograms as a function of simulation timestep and relative age (). by Jonathan E Kings (20818367)