The processing results of different denoising algorithms on bench images: (a) is the original image, (b) [22], (c) [21], (d) [23], (e) [24], (f) [25], (g) [7], (h) [26 by Wuyi Li (2984439)

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Functional pathways and evaluation of mutation between the two MRG-related risk groups. by Xuefen Lin (17785367)

Functional enrichment analysis of 24 differentially expressed aging-related genes. by Yong Zhou (23772)

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BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…<p>(A) After Step 3, the entire weight of the cell is accounted for by BOFdat. (B) Schematic description of the effect of adding a biomass precursor on the prediction of gene essentiality in the model. …”

Differential expression of DSRGs, PPI, and functional enrichment analysis for DSRGs in AMI. by Nan Huang (464390)

Test on the remeshing algorithm with <i>N</i> = 50 points. by Ondrej Maxian (8722185)

Differential analysis between the high- and low-risk score groups and functional analyses of DEGs. by Jian Wen (286688)

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Cooperativity and nonlinear amplification of synaptic input in cell type specific models. by Daniele Linaro (227790)

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

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Sparkler plot representation of functional impact of ARAF variants. by Alexis M. Thornton (11068403)

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PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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The processing results of different denoising algorithms on Tian ’anmen Square images: (a) is the original image, (b) [22], (c) [21], (d) [23], (e) [24], (f) [25], (g) [7], (h) [ by Wuyi Li (2984439)

The results of gene ontology analysis of the hub genes, including (a) biological process, (b) cellular components, (c) molecular functions, and (d) pathway analysis. by Devi Soorya Narayana Sasikumar (22331743)

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Boxplots for the maximising function of pattern matching technique. by Pratik Mullick (12851011)

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Patient-reported function, pain interference, mood, and sleep vary by cluster membership. by Benedict J. Alter (9745466)

The functional enrichment analysis of SOD2 and ZNF24 in OA. by Qiu Li (2127361)

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Second experiment results of the trained model. by Honglan Huang (341951)

Subjects: “…Cell Biology…”

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”