Preservation analysis of co-expressed modules of control networks were tested in the stress condition based on the median rank (A) and Zsummary (B) algorithms. by Bahman Panahi (7323578)

Subjects:

Comparison of different optimization algorithms. by Hang Zhao (143592)

Risk Groups Immune Infiltration Analysis by ssGSEA Algorithm. by Yongling Wang (13261656)

Proposed decision tree algorithm for thromboelastometry parameters to differentiate COVID-19 patients according to disease severity. by Rodrigo B. Aires (11949690)

Genetic Algorithm parameter search. by Elishai Ezra-Tsur (11888717)

“…<p><b>a.</b> Initiation of the genetic algorithm population with 100 randomly selected individuals, each with eight parameters (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1009754#sec010" target="_blank">Methods</a>). …”

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.…”

Cell-type marker genes and functional enrichment analysis. by Hailin Li (453832)

Subjects:

The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

Application of multi-scale community detection to subject-level and group-level resting state functional brain networks. by Arian Ashourvan (6685232)

Identification of hub genes from the PPI network by bottleneck algorithm using the cytoHubba Cytoscape plugin. by Cihat Erdogan (12529243)

Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

BNA Neuromarker scores as a function of LEDD. by Sharon Hassin-Baer (420354)

Subjects:

The repair algorithm restores the electrophysiological behaviour of cut and repaired mouse pyramidal cells and allows for better predictions of neuronal function in human neurons. by Moritz Groden (18018276)

Functional diversity indices of the accession grouping by Phaseolus spp. for each type of descriptor. by Diego Felipe Conejo-Rodríguez (18495740)

Subjects:

Compression rate of DDQR using real data. by Yujun Wang (1947196)

Subjects:

Functional enrichment analysis of DEGs. by Huiling Deng (4427161)

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

Local magnetization and susceptibility as a function of temperature from Metropolis simulations with the global archetype model. by Giulio Ruffini (383994)

Subjects:

The optimal contact/quarantine rates from the family of functions (4) and (5) for Hainan and Xinjiang. by Mengqi He (5753525)

“…(b, e) Root mean square error(), corresponding to fitting the time-dependent quarantine rate learned by TDINN algorithm using <i>q</i>₁(<i>t</i>), <i>q</i>₂(<i>t</i>) and <i>q</i>₃(<i>t</i>) in Hainan and Xinjiang. …”