Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

The details of the Scelestial algorithm. by Mohammad-Hadi Foroughmand-Araabi (6658772)

“…<p>The inputs to the Scelestial algorithm are a) a set of sequences <i>S</i>, b) the degree of restriction of the restricted Steiner tree <i>k</i>. …”

Brief sketch of the quasi-attraction/alignment algorithm. by Takayuki Niizato (162226)

“…The red and black arrows indicate the focal agent and its neighbor, respectively, while the green arrow indicates the next direction. (B) A sketch of the cover function, which returns the minimum cap on the interaction sphere , which covers all points <b><i>p</i></b>_<i>i</i> (for the mathematical definition, see the Section 2 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010869#pcbi.1010869.s008" target="_blank">S1 Appendix</a>). …”

Algorithm timings on a laptop computer with Intel i9–9900K CPU, 32GB RAM and an NVIDIA RTX2080 GPU. by Adam L. Tyson (10892822)

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Confusion matrices depict the performance of various ML algorithms. by Caroline L. Alves (14271413)

Immune microenvironment (A) Comparison of immune cell infiltration between the high-risk and low-risk groups using the ssGSEA algorithm. by Chengting Wu (21485612)

“…<p>(B) Comparison of immune function between the high-risk and low-risk groups using the ssGSEA algorithm. …”

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

Application of the smooth-index algorithm to the single-column connectome of the Drosophila optic lobe. by Alexander Borst (32619)

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Comparison of the smooth-index (‘SI’) algorithm with feedback arc set (‘FAS’) sorting. by Alexander Borst (32619)

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Percentage of transcript ineligibility as a function of context size. by Tariq Daouda (8300553)

Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

Subjects: “…Cell Biology…”

Workflow of the BOMS algorithm. by Ocima Kamboj (21533199)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. …”

Distinct responses of thorny and a-thorny cells to clustered dendritic inputs. by Daniele Linaro (227790)

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Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes. by Wenjun Zhao (644283)

“…<p>Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes.…”

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

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