Table 11_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…</p>Methods<p>A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute the GAPY−1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). …”

Table 4_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…</p>Methods<p>A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute the GAPY−1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). …”

Table 7_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…</p>Methods<p>A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute the GAPY−1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). …”

Table 3_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…</p>Methods<p>A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute the GAPY−1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). …”

Demonstration of algorithm design and operationalization. by Tobias S. Brett (5353364)

“…In panels (f–i), we present a step-by-step outline of how, for any time series data, our fitted algorithm functions as an early warning system. …”

Objective function trend with static and dynamic step size. by Ludovica Adacher (11238189)

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

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

Graphical summary and pseudo code of our gait signatures framework and algorithm. by Taniel S. Winner (17296029)

Confirmation of the <i>zion</i> locus. by McKenna Feltes (11242085)

Subjects:

Mapping of the <i>zion</i> locus. by McKenna Feltes (11242085)

Subjects:

<i>zion</i> WGS summary. by McKenna Feltes (11242085)

Subjects:

Mapping and confirmation of the <i>arches</i> locus. by McKenna Feltes (11242085)

Subjects:

<i>slc3a2a</i>^<i>zion</i> is a novel regulator of B-lp metabolism. by McKenna Feltes (11242085)

Subjects:

Convergence curves on the functions C22F1, C22F4, C22F5, C22F9, C22F10, C22F12, CBP, CSP, and TCP. by Mahmood A. Jumaah (22272371)

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Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

Subjects:

Effect of <i>|Δd</i>_<i>n</i><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)