Table_1_Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.DOCX by Gaoyan Zhang (450697)

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Functional enrichment results for the local module exploited for the seed gene <i>ECT2</i>. by Ehsan Pournoor (11250711)

Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

Functional enrichment analysis. by Junjie Su (368807)

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Supplementary Figure S1 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Left atrial volumes and functions. by Roxana Cristina Rimbas (12501324)

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Pathway activity and single-cell level functional states of CEDGs in cancer. by Yuxi Gu (11386081)

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

IFS performance with different classification algorithms and number of top features in the list yielded by MCFS. by Zhan Dong Li (12481390)

Data_Sheet_1_Combined brain network topological metrics with machine learning algorithms to identify essential tremor.docx by Qin Li (36669)

“…Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity.</p>Conclusion<p>These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.…”

Experiment: Application of Andean Condor Algorithm to Solve Many-Affine BBOB problems by Boris Almonacid (4110337)

“…</p><p dir="ltr"><br></p><p dir="ltr"><b>References</b></p><p dir="ltr">[1] Almonacid, B., & Soto, R. (2019). Andean Condor Algorithm for cell formation problems. …”

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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Average classification accuracies for fft and stack methods, LSTM, Bi-LSTM, and CNN algorithms. by Jamila Akhter (21144252)

Studies with ML classification algorithms for distinguishing ASD and ADHD groups described in subsection 1.2. by Caroline L. Alves (14271413)

Algorithm for generating virtual patients. by Adrianne L. Jenner (11133854)

“…<b>2)</b> The model evaluated is then simulated on this parameter set to obtain <i>y</i>(<i>t</i>, <i>p</i>). <b>3)</b> A simulated annealing algorithm is then used to determine a parameter set that optimises the objective function <i>J</i>(<i>p</i>) (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1009753#ppat.1009753.e059" target="_blank">Eq 17</a>). …”

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

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Table S1 from Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics by Michael Q. Ding (15079000)

Dice score for various <i>α</i> values (from the Eq 8) for the tracking of the CST and for the iFOD and PTT algorithms. by Thomas Durantel (20933812)

Subjects: “…Cell Biology…”

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

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Sub-model-specific gene sets with functional annotations and weights. by Josh L. Espinoza (10492448)