Step by step comparison of enrichment algorithms. by Nicholas Markarian (18251668)

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

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Visualization of the RITSS algorithm. by Julian Hecker (758791)

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

“…(B) Schematic description of the effect of adding a biomass precursor on the prediction of gene essentiality in the model. A simplified metabolic network composed of two linear pathways is depicted with its corresponding stoichiometric matrix S, in which the objective function is presented in the blue column (<i>v</i>_<i>obj</i>). …”

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

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

“…In `main.py`, the parameters for the TAMS algorithm are specified (trajectory time, time step, score function, number of particles, type of score threshold, maximum number of iterations, noise level etc.). …”

Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

Subjects: “…minimizing assignment algorithm…”

Overview of the initial cell candidate detection steps, from raw data to a cuboid of data fed into the classification network. by Adam L. Tyson (10892822)

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Algorithm operation steps. by Junyan Wang (4738518)

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Penalty functions used in different methods. by Souvik Seal (12437166)

Sorting time is nominally similar between automated and manual sorting, but automation enables walk-away functionality. by Diane M. Wiener (1956583)

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Step-by- step workflow of applied pipeline in current study. by Bahman Panahi (7323578)

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Functional impact of non-preserved modules-based gene ontology (biological processes category). by Bahman Panahi (7323578)

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

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

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. …”

Recombinant mapping narrows region of interest to identify a loss-of-function allele of <i>mttp.</i> by McKenna Feltes (11242085)

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Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

Table 12_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”