Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

“…</p> <p>The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. …”

Detailed information of benchmark functions. by Guangwei Liu (181992)

“…The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. …”

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

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Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

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Sensitivity analysis of the TV-BayesOpt algorithm performance at tracking a periodic temporal drift when the period of the drift is offset from the covariance function period anticipated by the TV-BayesOpt algorithm. by John E. Fleming (8533956)

“…<p>The performance of a scheduler (green line), the TV-BayesOpt with a periodic temporal covariance function (purple line) and the TV-BayesOpt with a periodic and smooth forgetting covariance algorithm were estimated by calculating the AUC value for the associated cumulative regret plot for each implementation at different offset in the true temporal period.…”

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)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

Flowchart for Algorithm 2. by Sagvan Y. Musa (17797611)

Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

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Smooth function of sample size. by Muhammad Atif (2575789)

Ridgeline plot for the probability density function of intracranial pressure predictions derived from the Extreme Gradient Boost algorithm of the arterial and venous pulsation data. by Anmar Abdul-Rahman (688431)

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

Function graph and algorithm iterative graph. by WanRu Zhao (18980374)

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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)

“…Lower panel: The signal detection algorithm constructs neurometric functions using numerical data from all <i>N</i>_<i>in</i> neuronal units. …”

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm by Gelio Alves (51850)

“…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …”

Idea of the search for a domain/motif within the range from 1 to W positions (in the window) instead of an only predetermined position. by Alexander V. Spirov (11302085)

Algorithm parameter levels. by Peng Yao (448155)

“…Alternatively, the formulated objective functions often deviate from real-world scenarios. …”

Sensitivity analysis of the TV-BayesOpt algorithm with a forgetting (orange line) or a forgetting-periodic (blue line) covariance function for a range of ε values. by John E. Fleming (8533956)

“…<p>Incorporation of prior knowledge of the temporal variation in the objective function optimum value (blue line) resulted in improved TV-BayesOpt algorithm performance than implementing a forgetting covariance function (orange line) alone. …”

Smoothing curve from GAM model in which the survival of smaller cluster has been modeled as a smooth function of the sample size. by Muhammad Atif (2575789)