Table 2_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 14_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 5_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 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 1_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). …”

The used test functions in the evaluation. by Xuan Chen (158617)

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

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

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. …”

MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

Flowchart for the genetic algorithm used in this article. by Behnam Kia (6111551)

Workflow overview illustrating the primary steps of the KRSA pipeline. by Erica A. K. DePasquale (11851483)

Images of partial benchmark functions. by ZeSheng Lin (20501356)

Subjects:

The proposed fuzzy model for objective function in CCR. by Zhou He (10330007)