Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Summary statistics for the number of function evaluations—Phase III. by Fadi K. Dib (5204807)

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Fig 3 - by Mona Selim (12969227)

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Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<p>Among 46 DEGs whose expression significantly changed in A549 and BEAS-2B cell lines, the core genes were selected using three algorithms: MCC, MNC, and DEGREE. <b>(A)</b> The top 10 genes were prioritized using MCC analysis, which identifies the largest clique within the PPI network and selects the central proteins within the clique. …”

GA and loss function (Loss_L) parameters are optimized using their search spaces through the hyperparameter tuning. by Burcu Ozek (16949357)

Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods by Xubin Wang (1861147)

“…According to the predefined donor–acceptor (DA) library to build an AD_<i>n</i>-type TADF candidate, we utilized the chemical notation language SMILES code to generate a TADF molecule and apply the RDKit program to produce the initial 3D molecular structure. A combined fitness function is proposed to evaluate the performance of the functional-lead TADF molecule. …”

Modeling Molecularly Imprinted Nanoparticles with LNKD: A Resource Efficient Algorithm for Polymer Cross-Linking by Emma Stevens (22257358)

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Flowchart of P&O-based MPPT algorithm. by Luqman Khan (17921419)

Evaluation of the optimization algorithm using mock microbiome reference values. by Juan Ricardo Velasco-Álvarez (16859181)

“…<p>A: Evolution of fraction community distribution concerning the cycles of adaptation enforced by the gradient descendant algorithm using a value of <i>α</i> = 0.125 and a mock microbiome composition; B: Comparison of pie charts for the mock reference biomass percentage distributions (left) and the obtained with the modeling strategy based on the gradient descent algorithm (right). …”

Evaluation of the optimization algorithm using clinical microbiome reference values. by Juan Ricardo Velasco-Álvarez (16859181)

“…<p>A: Evolution of biomass fraction distribution for the cycles of adaptation enforced by the gradient descendant algorithm using a value of <i>α</i> = 0.125 and a microbiome composition from Mexican patients published by [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0290082#pone.0290082.ref020" target="_blank">20</a>]; B: Comparison of pie charts for the clinical reference biomass percentage distributions (left) and the obtained with the modeling strategy based on the gradient descent algorithm (right); C: Performance functional (<i>J</i>) evolution using clinical data from healthy Mexican patients for the simulation.…”

The empirical distribution function and fitted distribution functions for data sets <i>Q</i> and <i>T</i>. by Salem A. Alyami (10572611)

The top 10 genes in the PPI network were calculated using five algorithms. by Fengjun Zhang (5878169)

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The parallel coordinates of the non-dominated front obtained by each algorithm (used in the comparison) on DTLZ3 and DTLZ7 with 3 objectives. by Heba Askr (15572851)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Top-ranked hub genes identified by 5 cytoHubba algorithms. by Jun Li (6494)

Source code for GeCo3 compression rate calculation using simulated data. by Yujun Wang (1947196)

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Source code for GeCo3 compression rate calculation using real data. by Yujun Wang (1947196)

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The GOR4 and SOPMA algorithm findings. by Ahmad Abdullah Mahdeen (19704401)

Functional enrichment analysis. by Junjie Su (368807)

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Plot of coverage attenuation function. by Junjie He (700642)

Subjects: “…coverage attenuation function…”