The Experts below are selected from a list of 264990 Experts worldwide ranked by ideXlab platform
Gota Morota - One of the best experts on this subject based on the ideXlab platform.
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ShinyGPAS: interactive genomic Prediction Accuracy simulator based on deterministic formulas
Genetics selection evolution : GSE, 2017Co-Authors: Gota MorotaAbstract:Background Deterministic formulas for the Accuracy of genomic Predictions highlight the relationships among Prediction Accuracy and potential factors influencing Prediction Accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control Prediction Accuracy.
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ShinyGPAS: interactive genomic Prediction Accuracy simulator based on deterministic formulas
Genetics Selection Evolution, 2017Co-Authors: Gota MorotaAbstract:AbstractBackgroundDeterministic formulas for the Accuracy of genomic Predictions highlight the relationships among Prediction Accuracy and potential factors influencing Prediction Accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control Prediction Accuracy.ResultsThe software to simulate deterministic formulas for genomic Prediction Accuracy was implemented in R and encapsulated as a web-based Shiny application. Shiny genomic Prediction Accuracy simulator (ShinyGPAS) simulates various deterministic formulas and delivers dynamic scatter plots of Prediction Accuracy versus genetic factors impacting Prediction Accuracy, while requiring only mouse navigation in a web browser. ShinyGPAS is available at: https://chikudaisei.shinyapps.io/shinygpas/.ConclusionShinyGPAS is a shiny-based interactive genomic Prediction Accuracy simulator using deterministic formulas. It can be used for interactively exploring potential factors that influence Prediction Accuracy in genome-enabled Prediction, simulating achievable Prediction Accuracy prior to genotyping individuals, or supporting in-class teaching. ShinyGPAS is open source software and it is hosted online as a freely available web-based resource with an intuitive graphical user interface.
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ShinyGPAS: Interactive genomic Prediction Accuracy simulator based on deterministic formulas
2017Co-Authors: Gota MorotaAbstract:Deterministic formulas highlight the relationships among Prediction Accuracy and potential factors influencing Prediction Accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control Prediction Accuracy. Results: The software to simulate deterministic formulas for genomic Prediction Accuracy was implemented in R and encapsulated as a web-based Shiny application. ShinyGPAS (Shiny Genomic Prediction Accuracy Simulator) simulates various deterministic formulas and delivers dynamic scatter plots of Prediction Accuracy vs. genetic factors impacting Prediction Accuracy, while requiring only mouse navigation in a web browser. ShinyGPAS is available at: https://chikudaisei.shinyapps.io/shinygpas. Conclusion: ShinyGPAS is a shiny-based interactive genomic Prediction Accuracy simulator using deterministic formulas. It can be used for interactively exploring potential factors influencing Prediction Accuracy in genome-enabled Prediction, simulating achievable Prediction Accuracy prior to genotyping individuals, or supporting in-class teaching. ShinyGPAS is open source software and it is hosted online as a freely available web-based resource with an intuitive graphical user interface.
Jerker Rönnberg - One of the best experts on this subject based on the ideXlab platform.
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Comprehension Calibration and Recall Prediction Accuracy of Texts: Reading Skill, Reading Strategies, and Effort.
Journal of Educational Psychology, 1995Co-Authors: Å Gillström, Jerker RönnbergAbstract:High school students at 3 levels of verbal skill rated their own recall (Prediction accuray) and comprehension (calibration Accuracy) of 3 expository texts accompanied by 3 different sets of instructions. All sets of instructions emphasized reading for understanding, and two of them also involved key words (given or personally selected), which were to be used during study. Students assessed which instructions they preferred and estimated their general verbal and memory skills. Three major results were obtained: (a) Students seemed to assess their general verbal and memory skills quite well. (b) Acceptable levels of comprehension calibration and recall Prediction Accuracy were found. Verbal-skill differences were found for recall Prediction Accuracy but not for comprehension calibration Accuracy. (c) Students had study preference-the most preferred way to study increased performance but reduced Prediction Accuracy.
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Prediction Accuracy of text recall: Ease, effort and familiarity
Scandinavian journal of psychology, 1994Co-Authors: Å Gillström, Jerker RönnbergAbstract:Prediction Accuracy of text recall was studied in two experiments. Text characteristics (i.e., consistency and distinctiveness) were manipulated in Experiment 1, and familiarity with the reading-task in Experiment 2. The results were also analyzed and discussed in terms of easy processing (Experiment 1), and in terms of increased and more active processing (Experiment 2). Text characteristics did not affect Prediction Accuracy. However, being familiar with the reading-task led to good and long-lasting Prediction Accuracy. Thus, subjects reading a school-book text, instructed to learn the contents of it demonstrated reliable memory awareness, both for immediate recall and for delay of one week. It was also suggested that increased processing demands and active reading enhances Prediction Accuracy.
Julius Van Der Werf - One of the best experts on this subject based on the ideXlab platform.
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Estimation of genomic Prediction Accuracy from reference populations with varying degrees of relationship.
PloS one, 2017Co-Authors: Sang Hong Lee, Sam Clark, Julius Van Der WerfAbstract:Genomic Prediction is emerging in a wide range of fields including animal and plant breeding, risk Prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic Prediction Accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as ‘unrelated’ individuals from the wider population in the genomic Prediction. The various sources of information were modeled as different populations with different effective population sizes (N). Both the effective number of chromosome segments (M) and N are considered to be a function of the data used for Prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic Prediction Accuracy than lesser related individuals. We also illustrate that when Prediction relies on closer relatives, there is less improvement in Prediction Accuracy with an increase in training data or marker panel density. We release software that can estimate the expected Prediction Accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic Prediction (before or after collecting data) in animal, plant and human genetics.
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Estimation Of Genomic Prediction Accuracy From Reference Populations With Varying Degrees Of Relationship
2017Co-Authors: Sang Hong Lee, Sam Clark, Julius Van Der WerfAbstract:We present a theoretical framework for genomic Prediction Accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as unrelated individuals from the wider population, assuming they all come from the same homogeneous population. The various sources of information were modeled as different populations with different effective population sizes (Ne). With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic Prediction Accuracy than lesser related individuals. However, the number of individuals from the wider population can be far greater than that of close relatives. We validate our theory with analysis of real data, and illustrate that the variation in genomic relationships with the target, rather than the variation in genomic relationship as a deviation for the expected relationship, is a predictor of the information content of the reference set and information from pedigree relationships is then naturally included in the Prediction framework. Both the effective number of chromosome segments (Me) and Ne are considered to be a function of the data used for Prediction rather than being population parameters. We illustrate that when Prediction also relies on closer relatives, there is less improvement in Prediction Accuracy with an increase in training data or marker panel density. We release software that can estimate the expected Prediction Accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic Prediction (i.e. before collecting data) in animal, plant and human genetics.
Jean-luc Jannink - One of the best experts on this subject based on the ideXlab platform.
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Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population.
The plant genome, 2015Co-Authors: Ahmad H. Sallam, Jean-luc Jannink, Jeffrey B. Endelman, Kevin P. SmithAbstract:Prediction Accuracy of genomic selection (GS) has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several Prediction models in a dynamic barley breeding population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP) markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training population composition on Prediction Accuracy over time. We found little difference in Prediction Accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased Prediction (RR-BLUP), to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in Accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The Prediction Accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium, LD) as well as characteristics of the trait (genetic architecture and heritability, H2 ). Fixation of markers associated with a trait over time was most clearly associated with reduced Prediction Accuracy for the mycotoxin trait DON. Higher trait H2 in the training population and simpler trait architecture were associated with greater Prediction Accuracy.
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multiple trait genomic selection methods increase genetic value Prediction Accuracy
Genetics, 2012Co-Authors: Jean-luc JanninkAbstract:Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the Prediction Accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher Prediction Accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.
Å Gillström - One of the best experts on this subject based on the ideXlab platform.
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Comprehension Calibration and Recall Prediction Accuracy of Texts: Reading Skill, Reading Strategies, and Effort.
Journal of Educational Psychology, 1995Co-Authors: Å Gillström, Jerker RönnbergAbstract:High school students at 3 levels of verbal skill rated their own recall (Prediction accuray) and comprehension (calibration Accuracy) of 3 expository texts accompanied by 3 different sets of instructions. All sets of instructions emphasized reading for understanding, and two of them also involved key words (given or personally selected), which were to be used during study. Students assessed which instructions they preferred and estimated their general verbal and memory skills. Three major results were obtained: (a) Students seemed to assess their general verbal and memory skills quite well. (b) Acceptable levels of comprehension calibration and recall Prediction Accuracy were found. Verbal-skill differences were found for recall Prediction Accuracy but not for comprehension calibration Accuracy. (c) Students had study preference-the most preferred way to study increased performance but reduced Prediction Accuracy.
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Prediction Accuracy of text recall: Ease, effort and familiarity
Scandinavian journal of psychology, 1994Co-Authors: Å Gillström, Jerker RönnbergAbstract:Prediction Accuracy of text recall was studied in two experiments. Text characteristics (i.e., consistency and distinctiveness) were manipulated in Experiment 1, and familiarity with the reading-task in Experiment 2. The results were also analyzed and discussed in terms of easy processing (Experiment 1), and in terms of increased and more active processing (Experiment 2). Text characteristics did not affect Prediction Accuracy. However, being familiar with the reading-task led to good and long-lasting Prediction Accuracy. Thus, subjects reading a school-book text, instructed to learn the contents of it demonstrated reliable memory awareness, both for immediate recall and for delay of one week. It was also suggested that increased processing demands and active reading enhances Prediction Accuracy.
