Model selection genetic. Medical search. Frequent questions

Predictive methods are diverse, and there is no superior model for every prediction problem [ 7 ]. (hindawi.com)
Instead of building a predictive model of the response given combinations of predictors, we start by modeling the conditional distribution of predictors given partitions based on responses. (harvard.edu)
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. (springer.com)
Comparing the best models, with all predictive variables, the machine learning technique related to random forest led to 87% accuracy, whereas logistic regression and linear discriminant analysis led to 69% and 50% accuracy, respectively, in the testing sample. (researchgate.net)

The biggest strength of the text is that it touches on a variety of topics from a wide range of mathematical subdisciplines (e.g., mathematical modeling, discrete dynamical systems, linear algebra, and probability,) while following a coherent and logical pathway through an interesting set of biological topics. (maa.org)
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. (mit.edu)

For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. (nature.com)
The personality traits of extraversion and neuroticism are two of the five higher-order personality factors that are consistently identified in dimensional models of personality. (nature.com)
This paradox, that cognitive ability and personality appear to be under selective pressure yet retain heritable variation, could be resolved if rare variants, which are less amenable to selection, are found to play a major role in the genetic contribution to variance in these traits. (nature.com)
Polygenic scores create the potential for existing embryo selection technologies to be used to select for a wider range of predicted genetically influenced characteristics including continuous traits. (bmj.com)
We examine how these three models would apply to the prediction of non-disease traits such as intelligence. (bmj.com)
Polygenic risk scores or polygenic scores (PS) analyse an individual's genome, aggregating thousands of genes, to estimate genetic tendency towards particular traits and diseases. (bmj.com)
Explore natural selection and the way in which environmental change affects the genetic traits of a population. (carolina.com)
In order to use selection to genetically improve traits we need reliable estimates of genetic parameters. (nofima.no)
Meyer K., Carrick M.J., Donnelly B.J.P., Genetic parameters for growth traits of Australian beef cattle from a multi-breed selection experiment, J. Anim. (gse-journal.org)
In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. (lu.se)

Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. (nature.com)
We introduce a new systematic approach to the Wright-Fisher model of population genetics based on the free energy functional. (mpg.de)

To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. (hindawi.com)
Harik, G.R.: Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. (springer.com)
Syswerda, G.: Uniform crossover in genetic algorithms. (springer.com)
Sastry, K.: Evaluation-relaxation schemes for genetic and evolutionary algorithms. (springer.com)
Lindstrom M.J., Bates D.M., Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data, J. Amer. Stat. Ass. (gse-journal.org)
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. (mit.edu)
This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. (mit.edu)
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. (mit.edu)
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. (mit.edu)
Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. (mit.edu)
Genetic algorithms (GAs) are of increasing interest, both as computational models of natural systems and as algorithmic techniques for problem-solving. (mit.edu)
Melanie Mitchell has written an outstanding-and needed-new text for the burgeoning field for genetic algorithms. (mit.edu)

Marjoram, P. & Tavaré, S. Modern computational approaches for analyzing molecular-genetic-variation data. (nature.com)

October 31, 2022 -- A genetic timestamping mouse model has shown the rate at which antibody-producing cells accumulate and disappear after vaccination, which could ultimately allow researchers to be able to increase the longevity of immunity from vaccines. (scienceboard.net)

2) Nonlinear equations: modelling with nonlinear rates, Michaelis-Menten-kinetics, unique existence of solutions, linearisation at equilibria, stability, preservation of positivity and funnel theorems, the phase plane method, simple examples of bifurcation scenarios. (bath.ac.uk)
However, one of these models' deficiencies is the inability to depict nonlinear relationships because of the assumption's limitations [ 10 ]. (hindawi.com)
Chapter 3 (Nonlinear Models of Interaction) introduces the dynamics of interacting populations, starting with a simple predator-prey model. (maa.org)
Efficient design for dose-response modeling is challenging due to the special features of toxicity data, i.e., the possibly nonlinear nature of dose-response curves and the typical variance heterogeneity (non-constant variance) involved. (cdc.gov)

However, Kimura's argument confused the lag load with the substitutional load, using the former when it is the latter that in fact sets the maximal rate of evolution by natural selection. (wikipedia.org)
More recent "travelling wave" models of rapid adaptation derive a term called the "lead" that is equivalent to the substitutional load, and find that it is a critical determinant of the rate of adaptive evolution. (wikipedia.org)
Loeske E. B. Kruuk , Jon Slate , Josephine M. Pemberton , Sue Brotherstone , Fiona Guinness , and Tim Clutton-Brock "ANTLER SIZE IN RED DEER: HERITABILITY AND SELECTION BUT NO EVOLUTION," Evolution 56(8), 1683-1695, (1 August 2002). (bioone.org)
We used the unique in vitro evolution model of F32-ART parasites selected from the African F32-Tanzania clonal line by using multiple dose-escalating artemisinin pressure to study the effect of extended artemisinin pressure on susceptibility to other antimalarial drugs. (cdc.gov)
Thus, the F32-ART experimental evolution model proved to be highly relevant in understanding P. falciparum artemisinin resistance in the field. (cdc.gov)
How About a New Theory of Evolution with Less Natural Selection? (scienceblogs.com)
Every competent evolutionary biologist since the 1970s has embraced the mathematics of Kimura and Ohta and knows that evolution is not solely driven by selection, and at the same time, they have not shouted "Revolution! (scienceblogs.com)
Mathematical Models in Biology: An Introduction is an introductory textbook in discrete mathematical modeling covering a wide variety of biological topics: dynamic models of population growth, models of molecular evolution, the construction of phylogenetic trees, genetics, and infectious disease modeling. (maa.org)
Be it resolved that the genetic and fossil evidence supports the evolution model and refutes the biblical creation model. (ubc.ca)

In asexual populations, the stochastic accumulation of mutation load is called Muller's ratchet, and occurs in the absence of beneficial mutations, when after the most-fit genotype has been lost, it cannot be regained by genetic recombination. (wikipedia.org)
Moreover, the analyzed populations exhibited a strong genetic structure in accordance with their geographic distribution, and can be placed into three genetic clusters: (1) Amarillo plus Chenhaló in the upper Grijalva basin, (2) Jataté, and (3) Tzaconejá, both in the upper Usumacinta basin. (peerj.com)
Chapter 1 (Dynamical Modeling with Difference Equations) covers discrete models of single populations, starting with the exponential growth model and proceeding on to the logistic growth model, before discussing the different types of long-term behavior (equilibrium points, n-cycles, and chaos. (maa.org)
Chapter 2 (Linear Models of Structured Populations) focuses primarily on discrete models of a single population that is partitioned into subpopulations. (maa.org)
Biostatistician and evolutionary biologist Jeffrey Townsend of Yale School of Public Health, Vincent Cannataro of Emmanuel College and Jeffrey Mandell of Yale devised a method inspired by evolutionary models of natural selection in wild populations to quantify how much every so-called point mutation, or change to a single DNA letter, in a tumor contributes to driving its growth. (scientificamerican.com)

Title : A Statistical Model for Assessing Genetic Susceptibility as a Risk Factor in Multifactorial Diseases: Lessons from Occupational Asthma Personal Author(s) : Demchuk, Eugene;Yucesoy, Berran;Johnson, Victor J.;Andrew, Michael;Weston, Ainsley;Germolec, Dori R.;De Rosa, Christopher T.;Luster, Michael I. (cdc.gov)
Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes. (who.int)

Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. (nature.com)
This related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyvaskyla. (jyu.fi)

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. (nature.com)
Thus, the objectives of this study were twofold: 1) to compare four alternative non-linear models: Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial based on their goodness of fit for quantifying genetic variation, as well as heritability for tick-count and 2) to investigate potential response to selection against tick-count based on truncation selection given the estimated genetic parameters from the best fit model. (nofima.no)
Meyer K., Random regressions to model phenotypic variation in monthly weights of Australian beef cows, Livest. (gse-journal.org)
The processed data consists of tens of thousands of growth curves with a complex hierarchical structure requiring sophisticated statistical modelling of genetic independence, genetic interaction (epistasis), and variation at multiple levels of the hierarchy. (lu.se)
Competing causes of mortali- or all of the animal species tested genetic variation, health status, life ty may prevent the development of experimentally. (who.int)

Meyer K., An "average information'' restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices, Genet. (gse-journal.org)
Meyer K., Estimates of genetic and phenotypic covariance functions for postweaning growth and mature weight of beef cows, J. Anim. (gse-journal.org)

However, it could be a candidate for genomic selection. (usda.gov)
Marker assisted selection and genomic selection are ways to select for resistance without disease phenotyping in early generations. (usda.gov)

To introduce basic approaches to the analysis of differential-equation/difference-equation models. (bath.ac.uk)
Existing regulatory approaches include 'disease-based' models which limit embryo selection to avoiding disease characteristics (employed in various formats in Australia, the UK, Italy, Switzerland and France, among others), and 'laissez-faire' or 'libertarian' models, under which embryo testing and selection remain unregulated (as in the USA). (bmj.com)
Genetic and phenotypic approaches are introduced that conceptualize, document and quantify selection and its outcome. (lu.se)

Linear combinations of those coefficients define variance components for the additive genetic variance at given points of the trajectory. (gse-journal.org)
General intelligence has been found to be heritable, with twin and family studies estimating that 50 to 80% [ 5 ] of phenotypic variance is due to additive genetic factors, a proportion that increases with age from childhood to adulthood [ 6 ]. (nature.com)

Currently, the use of polygenic scores for embryo selection is subject to existing regulations concerning embryo testing and selection. (bmj.com)
We introduce a novel 'Welfarist Model' which limits embryo selection according to the impact of the predicted trait on well-being. (bmj.com)
Indeed, polygenic scores exist to predict future intelligence, and there have been suggestions that they will be used to make predictions within the normal range in the USA in embryo selection. (bmj.com)

However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0 and 15% for personality variables. (nature.com)
By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. (nature.com)
We present estimates of the selection on and the heritability of a male secondary sexual weapon in a wild population: antler size in red deer. (bioone.org)
The resulting estimates of variance components and high heritability (0.32) led us to conclude that genetic determinism is relevant on tick count. (nofima.no)

Next, we combine the forward and inverse modeling perspectives under the Bayesian framework to detect pleiotropic and epistatic effects in effects in expression quantitative loci (eQTLs) studies. (harvard.edu)
We augment the Bayesian partition model proposed by Zhang et al. (harvard.edu)
Finally, we study the application of Bayesian partition models in the unsupervised learning of transcription factor (TF) families based on protein binding microarray (PBM). (harvard.edu)
A modified version of the Bayesian information criterion is developed for building a multilocus model that. (lu.se)
A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. (lu.se)
Starting from simple modelling of individual growth curves, a Bayesian hierarchical model can be built with variable selection indicators for inferring pairs of genes that genetically interact. (lu.se)
As a result, Bayesian concepts and models are nearly always explained using Frequentist language. (lu.se)

Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. (jyu.fi)
This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. (jyu.fi)
In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. (jyu.fi)
These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. (slideshare.net)
Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. (springer.com)
This paper introduces the contributions of Paul B. Baltes (1939-2006), German psychologist, to the called Psychology of Development and Aging, represented by lifespan theory, model of selection, optimization and compensation, research on intellectual plasticity in adulthood and aging, as well as interdisciplinary investigations on advanced aging. (bvsalud.org)
Successful development and aging are based on selection of goals, optimization of means to accomplish these goals, and investment on compensations when means fail. (bvsalud.org)

They used a within-individual Cox regression model to eliminate selection bias, which meant each patient was used as their own control. (medpagetoday.com)

Graser H.-U., Smith S.P., Tier B., A derivative-free approach for estimating variance components in animal models by restricted maximum likelihood, J. Anim. (gse-journal.org)
Johnson D.L., Thompson R., Restricted maximum likelihood estimation of variance components for univariate animal models using sparse matrix techniques and average information, J. Dairy Sci. (gse-journal.org)

Fig. 1: Modeling patterns of subclonal selection in sequencing data. (nature.com)
In the version of this article originally published, in the "Theoretical framework of subclonal selection" section of the main text, ref. 11 instead of ref. 19 should have been cited at the end of the phrase "Our previously presented frequentist approach to detect subclonal selection from bulk sequencing data involves an R 2 test statistic. (nature.com)
Assess this model critically, and to suggest alterations or elaborations of proposed model in light of discrepancies between model predictions and observed data or failures of the model to exhibit correct qualitative behaviour. (bath.ac.uk)
We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. (nature.com)
Firstly, using a recently developed analytic design for combined pedigree and genome-wide molecular genetic data, we test whether rare genetic variants, CNVs, and structural variants make an additional contribution to the genetic variance in intelligence, neuroticism, and extraversion. (nature.com)
Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. (inderscience.com)
Advances in data gathering, distribution and analysis have also created a need for an application of intelligent data analysis techniques to solve business modelling problems. (inderscience.com)
Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. (slideshare.net)
Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. (slideshare.net)
Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. (slideshare.net)
Variable selection methods play important roles in modeling high-dimensional data and are key to data-driven scientific discoveries. (harvard.edu)
The augmented partition model significantly improves the power in detecting eQTLs compared to previous methods in both simulations and real data examples pertaining to yeast. (harvard.edu)
However, tick-count data is a discrete variable and, thus, standard procedures using linear models may not be appropriate. (nofima.no)
Our results showed that zero-inflated Poisson was the most parsimonious model for the analysis of tick count data. (nofima.no)
However, despite having different modeling frameworks, most of these studies use similar data: information from relatively big, audited and/or public companies (Matenda et al. (researchgate.net)
Foulley J.-L., Jaffrézic F., Robert-Granié C., EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis, Genet. (gse-journal.org)
EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. (mdpi.com)
Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. (lu.se)
The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies. (bvsalud.org)
In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration. (bvsalud.org)
High-throughput robotic genetic technologies can be used to study the fitness of many thousands of genetic mutant strains of yeast, and the resulting data can be used to identify novel genetic interactions relevant to a target area of biology. (lu.se)
The methods will be applied to data from experiments designed to highlight networks of genetic interactions relevant to telomere biology. (lu.se)
In this work, an experimental design procedure particularly suitable to toxicity data collection is developed to guide the dose selection and animal allocation in experiments. (cdc.gov)
Most appropriate dose-response and variance models are identified to describe the toxicity data. (cdc.gov)

Unfortunately, resistance seems controlled by multiple small effect genes, meaning blotch resistance is not a good candidate for marker assisted selection. (usda.gov)

Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWAS of unrelated individuals. (nature.com)
The reason is that the researchers did not have a way to quantify the selective effect of those large mutations in their evolutionary models, although they are working on methods to address that problem now. (scientificamerican.com)

2013) Reliability modelling of access point selection and handovers in heterogeneous wireless environments. (ntnu.no)

In 2021, we will begin genotyping families segregating for resistance with the hope of developing a genome selection model. (usda.gov)

In this thesis, we consider the problem of variable selection with interaction detection. (harvard.edu)
We use this inverse modeling perspective as motivation to propose a stepwise procedure for effectively detecting interaction with few assumptions on parametric form. (harvard.edu)

Carvalheira J.G.V., Blake R.W., Pollak E.J., Quaas R.L., Duran-Castro C.V., Application of an autoregressive process to estimate genetic parameters and breeding values for daily milk yield in a tropical herd of Lucerna cattle and in United States Holstein herds, J. Dairy Sci. (gse-journal.org)

Our study aimed at identifying SNPs associated with DGE and IGE for survival time, and comparing results from models that analyse survival time and repeated binomial survival. (springer.com)

Construct an initial mathematical model for a real world process. (bath.ac.uk)
To address this gap, we utilized mathematical model selection to explore insulin's selective regulatory mechanisms on blood amino acids and lipids, considering their temporal patterns after oral glucose ingestion. (biorxiv.org)

The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. (hindawi.com)
In order to solve the problem of low efficiency of conventional fuzzy test mining, a research method of Android system Service Vulnerability mining technology based on Genetic Algorithm (GA) is proposed. (scpe.org)

This is not a problem within mathematical models of genetic load, or for empirical studies that compare the relative value of genetic load in one setting to genetic load in another. (wikipedia.org)
The three stages of modelling:(a) model formulation, including the use of empirical information, (b) model fitting and (c) model validation. (bath.ac.uk)

Catherine received $50,000 for creating a novel hierarchical machine learning model that is able to predict adverse drug reactions with 91% accuracy, along with their underlying biological mechanisms. (societyforscience.org)

In GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume (pp. 1765-1772). (jyu.fi)

2010) to capture complex dependence structure among gene expression and genetic markers. (harvard.edu)

Sexually reproducing species are expected to have lower genetic loads. (wikipedia.org)
Here, we describe five hypotheses to explain the polymorphism of multiple antigens in a single pathogen species and highlight research relevant to our current models of thinking about multi-locus antigenic diversity. (nih.gov)
On the basis of our results, we propose the recognition of at least three evolutionarily significant units (ESUs) for the species and the urgent implementation of ex situ and in situ conservation and management efforts that consider the genetic background of the species. (peerj.com)
lymphoid tissue, and digestive tract), which the animal model captures the It can be difficult to parse out concordance has often been ob- range of potential human response reasons for lack of tumour site con- served among different species after to the particular agent tested. (who.int)

The same quantitative trait loci were identified with all models. (springer.com)
For example, IGE are less exposed to natural selection compared to DGE [ 5 ], and therefore we expect that some loci may have large effects for IGE. (springer.com)

Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. (slideshare.net)

How does natural selection work and how does selection bring about adaptations? (lu.se)
Different models and modes of speciation are explained, and the relationship between speciation and natural selection is examined. (lu.se)

Deleterious mutation load is the main contributing factor to genetic load overall. (wikipedia.org)
The Haldane-Muller theorem of mutation-selection balance says that the load depends only on the deleterious mutation rate and not on the selection coefficient. (wikipedia.org)
The intuition for the lack of dependence on the selection coefficient is that while a mutation with stronger effects does more harm per generation, its harm is felt for fewer generations. (wikipedia.org)
A slightly deleterious mutation may not stay in mutation-selection balance but may instead become fixed by genetic drift when its selection coefficient is less than one divided by the effective population size. (wikipedia.org)
From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence, and education is consistent with mutation-selection balance. (nature.com)
Sastry, K., Goldberg, D.E.: Designing competent mutation operator via probabilistic model building of neighborhoods. (springer.com)

In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. (nature.com)
We test whether genetic variants not in LD with genotyped single-nucleotide polymorphisms (SNPs) (including rare variants, copy number variants (CNVs) and structural variants) make a contribution to intelligence and personality differences using two separate methods. (nature.com)
Approximately 30,000 single nucleotide polymorphisms (SNPs) were included in the genome-wide association study (GWAS), using a linear mixed model for survival time, a linear mixed model and a generalized linear mixed model for repeated binomial survival (0/1). (springer.com)
Backwards elimination was used to determine phenotypic and genetic variance explained by SNPs. (springer.com)
These SNPs explained 1 to 6% of the phenotypic variance and 9 to 44% of the total genetic variance. (springer.com)

Here, we extend this approach for population-based GWAS in the direction of multimarker models. (lu.se)

An efficient genetic selection operator model based on probability ranking and combination is also presented to improve the sample coverage and fuzzy test efficiency. (scpe.org)

Application of these methods to the analysis of distinguished model cases. (bath.ac.uk)
There are plenty of exercises supporting each section, but many of the exercises involve the analysis of a model that is already set up for the student. (maa.org)
Seminal academic research has evaluated bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks). (researchgate.net)
In this study, we test machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event, and compare their performance with results from discriminant analysis, logistic regression, and neural networks. (researchgate.net)
Paired genetic analysis by next-generation sequencing of lung cancer and associated idiopathic pulmonary fibrosis. (cdc.gov)

Our results are consistent with the hypothesis that a heritable trait under directional selection will not evolve if associations between the measured trait and fitness are determined by environmental covariances: In red deer males, for example, both antler size and success in the fights for mates may be heavily dependent on an individual's nutritional state. (bioone.org)
This is one of the first large studies investigating the genetic architecture of a socially-affected trait. (springer.com)

Gilmour A.R., Thompson R., Cullis B.R., Average Information REML, an efficient algorithm for variance parameter estimation in linear mixed models, Biometrics 51 (1995) 1440-1450. (gse-journal.org)

You do know that evolutionary theory already includes multiple modes of evolutionary change beyond selection, and it has had them quantified and described since the 1930s, right? (scienceblogs.com)

The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection. (jyu.fi)
Efficient design of biological experiments for dose-response modeling in toxicology studies. (cdc.gov)
Thus, the design of experiments, i.e., the selection of experimental doses and the allocation of animals, plays an important role in the estimation of dose-response relationships. (cdc.gov)

We find that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. (researchgate.net)

There are currently no sound estimates of the number of children born with a serious congenital disorder attributable to genetic or environmental causes. (who.int)

Genetic load is the difference between the fitness of an average genotype in a population and the fitness of some reference genotype, which may be either the best present in a population, or may be the theoretically optimal genotype. (wikipedia.org)
Genetic load can also be seen as reduced fitness at the population level compared to what the population would have if all individuals had the reference high-fitness genotype. (wikipedia.org)
One problem with calculating genetic load is that it is difficult to evaluate either the theoretically optimal genotype, or the maximally fit genotype actually present in the population. (wikipedia.org)
Students model 5 generations of moths, gathering evidence of genotype and allele frequencies as environmental variables change. (carolina.com)
Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. (lu.se)

To address this question, we used mathematical modeling to identify the selective regulatory mechanisms of insulin on blood amino acids and lipids. (biorxiv.org)

While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem. (jyu.fi)
The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. (jyu.fi)

Harville D.A., Recursive estimation using mixed linear models with autoregressive random effects, in: Van Vleck L.D., Searle S.R. (Eds. (gse-journal.org)

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images. (nih.gov)

Anatomy7

Organisms23

Diseases2

Chemicals and Drugs26

Analytical, Diagnostic and Therapeutic Techniques and Equipment56

Psychiatry and Psychology10

Phenomena and Processes109

Disciplines and Occupations9

Anthropology, Education, Sociology and Social Phenomena3

Technology, Industry, Agriculture4

Humanities1

Information Science16

Health Care33

Predictive4

Dynamical systems2

Traits10

Population genetics2

Algorithms12

Computational1

20221

Nonlinear4

Evolution9

Populations5

Susceptibility2

Causal2

Variation5

Covariance functions2

Genomic2

Approaches3

Additive genetic2

Embryo3

Heritability4

Bayesian7

Optimization7

Regression model1

Variance components2

Data24

Genes1

Quantify2

Heterogeneous1

20211

Interaction2

Parameters1

Analyse1

Mathematical model2

Solve2

Empirical2

Hierarchical1

Proceedings1

Gene expression1

Species4

Loci2

Algorithm selection1

Natural Selection2

Mutation6

SNPs5

GWAS1

Fuzzy1

Analysis5

Trait2

REML1

Evolutionary change1

Experiments3

Prediction1

Estimates1

Genotype5

Regulatory1

Techniques2

Estimation1

Neural1