Distribution of objective function values solved by three algorithms. by Meilin Zhu (688698)

Algorithm results based on FE simulated likelihood functions. by Nicolas Herzig (9230425)

“…<p>(A) Nodule depth estimation by the algorithm with the likelihood functions obtained by FEM simulation. …”

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Comparison of different algorithms. by Jiandong Qiu (20389944)

Convergence curve of unimodal functions compared with the PSO family algorithms. by Yong Gao (29929)

Convergence curve of unimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

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One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. by Yasamin Tabatabaee (18095491)

The SCOPer algorithm can be run efficiently on multiple cores. by Nima Nouri (2183760)

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Convergence curve of shift function. by Yong Gao (29929)

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations. by Alexis M. Thornton (11068403)

“…<p>(A) Overview of the experimental approach (B) Schematic of the eVIP decision tree-based eVIP algorithm. …”

The signal detection algorithm for constructing a neurometric function (the probability of segregation as a function of time) generates acceptable buildup fits at <i>DF</i> = 1, 3, 6, 9. by Quynh-Anh Nguyen (847240)

“…<p>For comparison, see Micheyl et al (2005) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>]. Upper panel: mean spike counts <i>m</i>_{<i>t</i>;<i>DF</i>} (scatter points) at <i>A</i>-tone selective neurons in A1 during tone <i>B</i> were extracted from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>, (Fig.3A in Ref)]. …”

Output groupings of the 3 algorithms for a linear degradation chain of <i>N</i> = 3 metabolites. by Yuanchen Zhao (12905580)

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Algorithm of genetic workup. by Idoia Martinez de Lapiscina (16528261)

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