Results 21 to 30 of about 333,629 (311)

How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

open access: yesFrontiers in Plant Science, 2020
Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation.
Christian R. Werner   +9 more
doaj   +1 more source

Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit [PDF]

open access: yes, 2010
The aim of this study was to evaluate and quantify the importance of avoiding overlap between training and testing subsets of data when evaluating the effectiveness of predictions of genetic merit based on genetic markers.
G. Banos   +3 more
core   +1 more source

Protein structure prediction in genomics [PDF]

open access: yesBriefings in Bioinformatics, 2001
As the number of completely sequenced genomes rapidly increases, including now the complete Human Genome sequence, the post-genomic problems of genome-scale protein structure determination and the issue of gene function identification become ever more pressing.
openaire   +2 more sources

Prediction of operons in microbial genomes [PDF]

open access: yesNucleic Acids Research, 2001
Operon structure is an important organization feature of bacterial genomes. Many sets of genes occur in the same order on multiple genomes; these conserved gene groupings represent candidate operons. This study describes a computational method to estimate the likelihood that such conserved gene sets form operons.
M D, Ermolaeva, O, White, S L, Salzberg
openaire   +2 more sources

Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs

open access: yesAnimals, 2023
Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits.
Yiyi Liu   +12 more
doaj   +1 more source

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder [PDF]

open access: yes, 2015
Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk.
Caesar, Sian   +846 more
core   +1 more source

Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population

open access: yesAnimal, 2020
Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic.
S. Ye, H. Song, X. Ding, Z. Zhang, J. Li
doaj   +1 more source

AUC-RF: A New Strategy for Genomic Profiling with Random Forest [PDF]

open access: yes, 2011
Objective: Genomic profiling, the use of genetic variants at multiple loci simultaneously for the prediction of disease risk, requires the selection of a set of genetic variants that best predicts disease status.
Luz Calle, M.   +10 more
core   +1 more source

Pan-genomic open reading frames: A potential supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction.

open access: yesPLoS Genetics, 2020
Pan-genomic open reading frames (ORFs) potentially carry protein-coding gene or coding variant information in a population. In this study, we suggest that pan-genomic ORFs are promising to be utilized in estimation of heritability and genomic prediction.
Zhengcao Li, Henner Simianer
doaj   +1 more source

Maximum a posteriori Threshold Genomic Prediction Model for Ordinal Traits

open access: yesG3: Genes, Genomes, Genetics, 2020
Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data.
Abelardo Montesinos-López   +3 more
doaj   +1 more source

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