Search results for "model selection"

Anticipating the impact of pitfalls in kinetic biodegradation parameter estimation from substrate depletion curves of organic pollutants

2019

[EN] Accurate and reliable estimation of kinetic parameters of pollutant biodegradation processes is essential for environmental and health risk assessment. Common biodegradation models proposed in the literature, such as the nonlinear Monod equation and its simplified versions (e.g. Michaelis-Menten-like and first-order equations), are problematic in terms of accuracy of kinetic parameters due to the parameter correlation. However, a comparison between these models in terms of accuracy and reliability, related to data imprecision, has not been performed in the literature. This task is necessary, mainly because the model selection cannot be straightforward, as shown in this work. To facilit…

The effect of RNA substitution models on viroid and RNA virus phylogenies.

2018

Abstract Many viroids and RNA viruses have genomes that exhibit secondary structure, with paired nucleotides forming stems and loops. Such structures violate a key assumption of most methods of phylogenetic reconstruction, that sequence change is independent among sites. However, phylogenetic analyses of these transmissible agents rarely use evolutionary models that account for RNA secondary structure. Here, we assess the effect of using RNA-specific nucleotide substitution models on the phylogenetic inference of viroids and RNA viruses. We obtained data sets comprising full-genome nucleotide sequences from six viroid and ten single-stranded RNA virus species. For each alignment, we inferre…

Model‐based approaches to unconstrained ordination

2014

Summary Unconstrained ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained ordination can address this issue, and in this study, two types of models for ordination are proposed based on finite mixtu…

Detection of Allee effects in marine fishes: analytical biases generated by data availability and model selection

2017

The demographic Allee effect, or depensation, implies positive association between per capita population growth rate and population size at low abundances, thereby lowering growth ability of sparse populations. This can have far-reaching consequences on population recovery ability and colonization success. In the context of marine fishes, there is a widespread perception that Allee effects are rare or non-existent. However, studies that have failed to detect Allee effects in marine fishes have suffered from several fundamental methodological and data limitations. In the present study, we challenge the prevailing perception about the rarity of Allee effects by analysing nine populations of …

Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling.

2012

8 pages; International audience; Studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. It is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. To cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (CR-SEM) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. We employ Markov chain Monte Carlo sampling in a Bayesian frame…

Fruit size in relation to competition for resources: A common model shared by two species and several genotypes grown under contrasted carbohydrate l…

2012

International audience; Fruit size is one important criterion of fruit external quality affecting consumer acceptance. The effects of seed number on fruit size in two fleshy fruits, grape and tomato, of different genotypes and grown under distinct carbohydrate availability levels were analyzed with a model. The two-parameter model described within-fruit resource competition and was able to well represent the commonly observed decrease in fresh weight per seed along with the increase in number of seeds, regardless of species, genotypes, and carbohydrate levels that were evaluated in this study. However, carbohydrate levels largely modified the correlation between seed number and fresh weight…

Extreme minimal learning machine: Ridge regression with distance-based basis

2019

The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…

2019

Background Body fat and/or muscle composition influences prognosis in several cancer types. For advanced gastric and gastroesophageal junction cancer, we investigated which body composition parameters carry prognostic information beyond well-established clinical parameters using robust model selection strategy such that parameters identified can be expected to generalize and to be reproducible beyond our particular data set. Then we modelled how differences in these parameters translate into survival outcomes. Methods Fat and muscle parameters were measured on baseline computed tomography scans in 761 patients with advanced gastric or gastroesophageal junction cancer from the phase III EXPA…

Two-Stage Bayesian Approach for GWAS With Known Genealogy

2019

Genome-wide association studies (GWAS) aim to assess relationships between single nucleotide polymorphisms (SNPs) and diseases. They are one of the most popular problems in genetics, and have some peculiarities given the large number of SNPs compared to the number of subjects in the study. Individuals might not be independent, especially in animal breeding studies or genetic diseases in isolated populations with highly inbred individuals. We propose a family-based GWAS model in a two-stage approach comprising a dimension reduction and a subsequent model selection. The first stage, in which the genetic relatedness between the subjects is taken into account, selects the promising SNPs. The se…

Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

2016

Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value…