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Systematic Biology Apr 2022Phylogenetics has long relied on the use of orthologs, or genes related through speciation events, to infer species relationships. However, identifying orthologs is...
Phylogenetics has long relied on the use of orthologs, or genes related through speciation events, to infer species relationships. However, identifying orthologs is difficult because gene duplication can obscure relationships among genes. Researchers have been particularly concerned with the insidious effects of pseudoorthologs-duplicated genes that are mistaken for orthologs because they are present in a single copy in each sampled species. Because gene tree topologies of pseudoorthologs may differ from the species tree topology, they have often been invoked as the cause of counterintuitive results in phylogenetics. Despite these perceived problems, no previous work has calculated the probabilities of pseudoortholog topologies or has been able to circumscribe the regions of parameter space in which pseudoorthologs are most likely to occur. Here, we introduce a model for calculating the probabilities and branch lengths of orthologs and pseudoorthologs, including concordant and discordant pseudoortholog topologies, on a rooted three-taxon species tree. We show that the probability of orthologs is high relative to the probability of pseudoorthologs across reasonable regions of parameter space. Furthermore, the probabilities of the two discordant topologies are equal and never exceed that of the concordant topology, generally being much lower. We describe the species tree topologies most prone to generating pseudoorthologs, finding that they are likely to present problems to phylogenetic inference irrespective of the presence of pseudoorthologs. Overall, our results suggest that pseudoorthologs are unlikely to mislead inferences of species relationships under the biological scenarios considered here.[Birth-death model; orthologs; paralogs; phylogenetics.].
Topics: Gene Duplication; Models, Genetic; Phylogeny; Probability
PubMed: 34951639
DOI: 10.1093/sysbio/syab097 -
Osteoporosis International : a Journal... Feb 2011A country-specific FRAX® model was developed from the epidemiology of fracture and death in Belgium. Fracture probabilities were identified that corresponded to...
UNLABELLED
A country-specific FRAX® model was developed from the epidemiology of fracture and death in Belgium. Fracture probabilities were identified that corresponded to currently accepted reimbursement thresholds.
INTRODUCTION
The objective of this study was to evaluate a Belgian version of the WHO fracture risk assessment (FRAX®) tool to compute 10-year probabilities of osteoporotic fracture in men and women. A particular aim was to determine fracture probabilities that corresponded to the reimbursement policy for the management of osteoporosis in Belgium and the clinical scenarios that gave equivalent fracture probabilities.
METHODS
Fracture probabilities were computed from published data on the fracture and death hazards in Belgium. Probabilities took account of age, sex, the presence of clinical risk factors and femoral neck bone mineral density (BMD). Fracture probabilities were determined that were equivalent to intervention (reimbursement) thresholds currently used in Belgium.
RESULTS
Fracture probability increased with age, lower BMI, decreasing BMD T-score and all clinical risk factors used alone or combined. The 10-year probabilities of a major osteoporosis-related fracture that corresponded to current reimbursement guidelines ranged from approximately 7.5% at the age of 50 years to 26% at the age of 80 years where a prior fragility fracture was used as an intervention threshold. For women at the threshold of osteoporosis (femoral neck T-score = -2.5 SD), the respective probabilities ranged from 7.4% to 15%. Several combinations of risk-factor profiles were identified that gave similar or higher fracture probabilities than those currently accepted for reimbursement in Belgium.
CONCLUSIONS
The FRAX® tool has been used to identify possible thresholds for therapeutic intervention in Belgium, based on equivalence of risk with current guidelines. The FRAX® model supports a shift from the current DXA-based intervention strategy, towards a strategy based on fracture probability of a major osteoporotic fracture that in turn may improve identification of patients at increased fracture risk. The approach will need to be supported by health economic analyses.
Topics: Aged; Aged, 80 and over; Algorithms; Belgium; Female; Humans; Male; Middle Aged; Models, Statistical; Osteoporotic Fractures; Risk Assessment; Risk Factors
PubMed: 20352409
DOI: 10.1007/s00198-010-1218-1 -
Statistics in Medicine Feb 2015Bivariate multinomial data such as the left and right eyes retinopathy status data are analyzed either by using a joint bivariate probability model or by exploiting...
Bivariate multinomial data such as the left and right eyes retinopathy status data are analyzed either by using a joint bivariate probability model or by exploiting certain odds ratio-based association models. However, the joint bivariate probability model yields marginal probabilities, which are complicated functions of marginal and association parameters for both variables, and the odds ratio-based association model treats the odds ratios involved in the joint probabilities as 'working' parameters, which are consequently estimated through certain arbitrary 'working' regression models. Also, this later odds ratio-based model does not provide any easy interpretations of the correlations between two categorical variables. On the basis of pre-specified marginal probabilities, in this paper, we develop a bivariate normal type linear conditional multinomial probability model to understand the correlations between two categorical variables. The parameters involved in the model are consistently estimated using the optimal likelihood and generalized quasi-likelihood approaches. The proposed model and the inferences are illustrated through an intensive simulation study as well as an analysis of the well-known Wisconsin Diabetic Retinopathy status data.
Topics: Biometry; Computer Simulation; Data Interpretation, Statistical; Diabetic Retinopathy; Humans; Likelihood Functions; Linear Models; Models, Statistical; Odds Ratio; Probability
PubMed: 25320019
DOI: 10.1002/sim.6333 -
Systematic Biology Jun 2005The Bayesian method for estimating species phylogenies from molecular sequence data provides an attractive alternative to maximum likelihood with nonparametric bootstrap... (Comparative Study)
Comparative Study
The Bayesian method for estimating species phylogenies from molecular sequence data provides an attractive alternative to maximum likelihood with nonparametric bootstrap due to the easy interpretation of posterior probabilities for trees and to availability of efficient computational algorithms. However, for many data sets it produces extremely high posterior probabilities, sometimes for apparently incorrect clades. Here we use both computer simulation and empirical data analysis to examine the effect of the prior model for internal branch lengths. We found that posterior probabilities for trees and clades are sensitive to the prior for internal branch lengths, and priors assuming long internal branches cause high posterior probabilities for trees. In particular, uniform priors with high upper bounds bias Bayesian clade probabilities in favor of extreme values. We discuss possible remedies to the problem, including empirical and full Bayesian methods and subjective procedures suggested in Bayesian hypothesis testing. Our results also suggest that the bootstrap proportion and Bayesian posterior probability are different measures of accuracy, and that the bootstrap proportion, if interpreted as the probability that the clade is true, can be either too liberal or too conservative.
Topics: Bayes Theorem; Classification; Computer Simulation; Data Interpretation, Statistical; Models, Genetic; Phylogeny; Plants; Probability
PubMed: 16012111
DOI: 10.1080/10635150590945313 -
Systematic Biology Jun 2022If all nucleotide sites evolved at the same rate within molecules and throughout the history of lineages, if all nucleotides were in equal proportion, if any nucleotide...
If all nucleotide sites evolved at the same rate within molecules and throughout the history of lineages, if all nucleotides were in equal proportion, if any nucleotide or amino acid evolved to any other with equal probability, if all taxa could be sampled, if diversification happened at well-spaced intervals, and if all gene segments had the same history, then tree building would be easy. But of course, none of those conditions are true. Hence, the need for evaluating the information content and accuracy of phylogenetic trees. The symposium for which this historical essay and presentation were developed focused on the importance of phylogenetic support, specifically branch support for individual clades. Here, I present a timeline and review significant events in the history of systematics that set the stage for the development of the sophisticated measures of branch support and examinations of the information content of data highlighted in this symposium. [Bayes factors; bootstrap; branch support; concordance factors; internode certainty; posterior probabilities; spectral analysis; transfer bootstrap expectation.].
Topics: Bayes Theorem; Nucleotides; Phylogeny; Probability
PubMed: 32915964
DOI: 10.1093/sysbio/syaa068 -
Forensic Science International May 2014Bayesian estimation applied to temperature based death time estimation was recently introduced as conditional probability distribution or CPD-method by Biermann and...
Bayesian estimation applied to temperature based death time estimation was recently introduced as conditional probability distribution or CPD-method by Biermann and Potente. The CPD-method is useful, if there is external information that sets the boundaries of the true death time interval (victim last seen alive and found dead). CPD allows computation of probabilities for small time intervals of interest (e.g. no-alibi intervals of suspects) within the large true death time interval. In the light of the importance of the CPD for conviction or acquittal of suspects the present study identifies a potential error source. Deviations in death time estimates will cause errors in the CPD-computed probabilities. We derive formulae to quantify the CPD error as a function of input error. Moreover we observed the paradox, that in cases, in which the small no-alibi time interval is located at the boundary of the true death time interval, adjacent to the erroneous death time estimate, CPD-computed probabilities for that small no-alibi interval will increase with increasing input deviation, else the CPD-computed probabilities will decrease. We therefore advise not to use CPD if there is an indication of an error or a contra-empirical deviation in the death time estimates, that is especially, if the death time estimates fall out of the true death time interval, even if the 95%-confidence intervals of the estimate still overlap the true death time interval.
Topics: Body Temperature; Forensic Medicine; Humans; Likelihood Functions; Postmortem Changes; Probability
PubMed: 24662512
DOI: 10.1016/j.forsciint.2014.02.016 -
Multivariate Behavioral Research 2022This paper presents a novel Bayesian variable selection approach that accounts for the sign of the regression coefficients based on multivariate one-sided tests. We...
This paper presents a novel Bayesian variable selection approach that accounts for the sign of the regression coefficients based on multivariate one-sided tests. We propose a truncated prior to specify a prior distribution of coefficients with anticipated signs in a given model. Informative priors for the direction of the effects can be incorporated into prior model probabilities. The best subset of variables is selected by comparing the posterior probabilities of the possible models. The new Bayesian one-sided variable selection procedure has higher chance to include relevant variables and therefore select the best model, if the anticipated direction is accurate. For a large number of candidate variables, we present an adaptation of a Bayesian model search method for the one-sided variable selection problem to ensure fast computation. In addition, a fully Bayesian approach is used to adjust the prior inclusion probability of each one-sided model to correct for multiplicity. The performance of the proposed method is investigated using several simulation studies and two real data examples.
Topics: Bayes Theorem; Computer Simulation; Probability
PubMed: 32869690
DOI: 10.1080/00273171.2020.1813067 -
Neural Computation Jul 2014We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a...
We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory (Kahneman & Tversky, 1979 ), for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.
Topics: Algorithms; Brain; Brain Mapping; Decision Making; Humans; Magnetic Resonance Imaging; Markov Chains; Models, Psychological; Nonlinear Dynamics; Oxygen; Probability; Reinforcement, Psychology; Risk
PubMed: 24708369
DOI: 10.1162/NECO_a_00600 -
Nederlands Tijdschrift Voor Geneeskunde Jun 2022Diagnostic prediction models can support the diagnostic process, both for experienced physicians and for physicians with little experience. More attention should be paid...
Diagnostic prediction models can support the diagnostic process, both for experienced physicians and for physicians with little experience. More attention should be paid to the incorporation of diagnostic prediction models in the electronic patient record, so that a more accurate probability estimate can be made without simplification to rounded sumscores. A uniform cut-off of sum scores with associated categorization is also undesirable, because it does not take the context of the individual patient sufficiently into account. In the case of a very strong gut feeling, the outcome of a diagnostic prediction model rule alone cannot be sufficient for further policy. Diagnostic prediction models 'only' generate individual objectively estimated probabilities; the clinical decision-making based on these probabilities always needs to be made by the doctor in shared decision making with the patient. Conflict of interest and financial support: none declared.
Topics: Humans; Probability
PubMed: 35736374
DOI: No ID Found -
Risk Analysis : An Official Publication... Dec 2006The question addressed in the present research is whether in naturalistic risky decision environments people are sensitive to information about the probability...
The question addressed in the present research is whether in naturalistic risky decision environments people are sensitive to information about the probability parameter. In Study 1, we showed that in naturalistic scenarios participants generally revealed little interest in obtaining information about the outcomes and probabilities. Moreover, participants asked fewer questions about probabilities for scenarios containing moral considerations. In Study 2, it was shown that, when supplied with information on probabilities, people could be sensitive to this information. This sensitivity depends on two factors. People were less sensitive to probabilities in scenarios perceived as containing ethical considerations. People were also less sensitive to probabilities when they were faced with a single-choice situation than when they were faced with a series of lotteries with different probabilities. This can be accounted for in terms of the evaluability principle.
Topics: Adult; Analysis of Variance; Choice Behavior; Decision Making; Decision Support Techniques; Humans; Models, Statistical; Models, Theoretical; Probability; Risk; Risk Assessment; Risk-Taking; Sensitivity and Specificity; Uncertainty
PubMed: 17184402
DOI: 10.1111/j.1539-6924.2006.00847.x