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PubMed1994

Passive smoking in the workplace: classical and Bayesian meta-analyses.

There are currently several classical and Bayesian methods of meta-analysis available for combining epidemiological results. We describe and compare these in a consistent framework, and apply them to published studies of the relative risk of lung cancer associated with exposure to environmental tobacco smoke in the workplace. We find that although all methods give reasonably similar combined estimates of relative risk of lung cancer associated with this exposure (none of which is significantly raised above unity, in either a frequentist or a Bayesian sense), the approximations arising from classical methods appear to be nonconservative and should be used with caution. The Bayesian methods, which account more explicitly for possible inhomogeneity in studies, give slightly lower estimates again of relative risk and wider posterior credible intervals, indicating that inference from the non-Bayesian approaches might be optimistic.

Bayes Theorem↗
PubMed1997

Detection of convergent and parallel evolution at the amino acid sequence level.

Adaptive evolution at the molecular level can be studied by detecting convergent and parallel evolution at the amino acid sequence level. For a set of homologous protein sequences, the ancestral amino acids at all interior nodes of the phylogenetic tree of the proteins can be statistically inferred. The amino acid sites that have experienced convergent or parallel changes on independent evolutionary lineages can then be identified by comparing the amino acids at the beginning and end of each lineage. At present, the efficiency of the methods of ancestral sequence inference in identifying convergent and parallel changes is unknown. More seriously, when we identify convergent or parallel changes, it is unclear whether these changes are attributable to random chance. For these reasons, claims of convergent and parallel evolution at the amino acid sequence level have been disputed. We have conducted computer simulations to assess the efficiencies, of the parsimony and Bayesian methods of ancestral sequence inference in identifying convergent and parallel-change sites. Our results showed that the Bayesian method performs better than the parsimony method in identifying parallel changes, and both methods are inefficient in identifying convergent changes. However, the Bayesian method is recommended for estimating the number of convergent-change sites because it gives a conservative estimate. We have developed statistical tests for examining whether the observed numbers of convergent and parallel changes are due to random chance. As an example, we reanalyzed the stomach lysozyme sequences of foregut fermenters and found that parallel evolution is statistically significant, whereas convergent evolution is not well supported.

Amino Acid Sequence↗
PubMed2005

Strange bayes indeed: uniform topological priors imply non-uniform clade priors.

While Bayesian analysis has become common in phylogenetics, the effects of topological prior probabilities on tree inference have not been investigated. In Bayesian analyses, the prior probability of topologies is almost always considered equal for all possible trees, and clade support is calculated from the majority rule consensus of the approximated posterior distribution of topologies. These uniform priors on tree topologies imply non-uniform prior probabilities of clades, which are dependent on the number of taxa in a clade as well as the number of taxa in the analysis. As such, uniform topological priors do not model ignorance with respect to clades. Here, we demonstrate that Bayesian clade support, bootstrap support, and jackknife support from 17 empirical studies are significantly and positively correlated with non-uniform clade priors resulting from uniform topological priors. Further, we demonstrate that this effect disappears for bootstrap and jackknife when data sets are free from character conflict, but remains pronounced for Bayesian clade supports, regardless of tree shape. Finally, we propose the use of a Bayes factor to account for the fact that uniform topological priors do not model ignorance with respect to clade probability.

Bayes Theorem↗
PubMed2025

Bayesian Phylogenetic Lineage Reconstruction with Loss of Heterozygosity Mutations Derived from Single-Cell RNA Sequencing.

Mutations are acquired frequently, such t`hat each cell's genome inscribes its history of cell divisions. Loss of heterozygosity (LOH) accumulates throughout the genome, offering large encoding capacity for phylogenetic inference of cell lineage.In this chapter, we demonstrate a method, using single-cell RNA sequencing, for reconstructing cell lineages from inferred LOH events in a Bayesian manner, annotating the lineage with cell phenotypes, and marking developmental time points based on X-chromosome inactivation. This type of retrospective analysis could be incorporated into scRNA-seq pipelines and was initially developed to investigate Emx1+ cortical projection neuron and glia lineages from C57Bl/6J (B6) and CAST/EiJ (CA) interstrain F1 mice, describing progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis.

Animals↗
PubMed2003

A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.

In applications that involve clustered data, such as longitudinal studies and developmental toxicity experiments, the number of subunits within a cluster is often correlated with outcomes measured on the individual subunits. Analyses that ignore this dependency can produce biased inferences. This article proposes a Bayesian framework for jointly modeling cluster size and multiple categorical and continuous outcomes measured on each subunit. We use a continuation ratio probit model for the cluster size and underlying normal regression models for each of the subunit-specific outcomes. Dependency between cluster size and the different outcomes is accommodated through a latent variable structure. The form of the model facilitates posterior computation via a simple and computationally efficient Gibbs sampler. The approach is illustrated with an application to developmental toxicity data, and other applications, to joint modeling of longitudinal and event time data, are discussed.

Animals↗
PubMed2005

[Phylogeny of chinese catfishes inferred from mitochondrial cytochrome b sequences].

The mitochondrial DNA cytochrome b gene was sequenced from 27 catfish species representing 11 families and 24 genera catfishes in China. Aligning with cytochrome b sequences of eight catfish species from North America and Africa retrieved from GenBank, and selecting Astyanax mexicanu, Cyprinus carpio, and Sardinops melanostictus as outgroups, we constructed a matrix of 38 DNA sequences. The phylogenetic trees were constructed by using Bayesian method and Maximum Parsimony (MP) method. The results showed that (1) there are three base pair deletions of mitochondrial cytochrome b gene compared with Characiformes, Cypriniformes, and Clupeiformes; (2) the representatives of Chinese catfish species form a monophyletic group; and (3) the molecular phylogenetic trees constructed with both methods suggest that the families Sisoridae, Akysidae and Amblycipitidae form a monophyletic group, and the families Clariidae, Schilbidae, Ariidae, Ictaluridae, Cranoglanididae, Pangasiidae, Siluridae, Claroteidae, and Bagridae also form a monophyletic group. The families Cranoglanididae from China and Ictaluridae from North America form a sister-group relationship, and the families Clariidae, Ictaluridae, Siluridae, and Sisoridae are obviously monophyletic groups. But the position of the family Plotosidae was not resolved by Bayesian analysis and maximum parsimony inference.

Animals↗
PubMed2006

Resolving the root of the avian mitogenomic tree by breaking up long branches.

Incomplete taxon sampling has been a major problem in resolving the early divergences in birds. Five new mitochondrial genomes are reported here (brush-turkey, lyrebird, suboscine flycatcher, turkey vulture, and a gull) and three break up long branches that tended to attract the distant reptilian outgroup. These long branches were to galliforms, and to oscine and suboscine passeriformes. Breaking these long branches leaves the root, as inferred by maximum likelihood and Bayesian phylogenetic analyses, between paleognaths and neognaths. This means that morphological, nuclear, and mitochondrial data are now in agreement on the position of the root of the avian tree and we can, move on to other questions. An overview is then given of the deepest divisions in the mitogenomic tree inferred from complete mitochondrial genomes. The strict monophyly of both the galloanseres and the passerines is strongly supported, leaving the deep six-way split within Neoaves as the next major question for which resolution is still lacking. Incomplete taxon sampling was also a problem for Neoaves, and although some resolution is now available there are still problems because current phylogenetic methods still fail to account for real features of DNA sequence evolution.

Animals↗
PubMed1993

Reporting Bayesian analyses of clinical trials.

Many clinicians wrongly interpret p-values as probabilities that treatment has an adverse effect and confidence intervals as probability intervals. Such inferences can be validly drawn from Bayesian analyses of trial results. These analyses use the data to update the prior (or pre-trial) beliefs to give posterior (or post-trial) beliefs about the magnitude of a treatment effect. However, for these methods to gain acceptance in the medical literature, understanding between statisticians and clinicians of the issues involved in choosing appropriate prior distributions for trial reporting needs to be reached. I focus on two types of prior that deserve consideration. The first is the non-informative prior giving standardized likelihood distributions as post-trial probability distributions. Their use is unlikely to be controversial among statisticians whilst being intuitively appealing to clinicians. The second type of prior has a spike of probability mass at the point of no treatment effect. Varying the magnitude of the spike illustrates the sensitivity of the conclusions drawn to the degree of prior scepticism in a treatment effect. With both, graphical displays provide clinical readers with the opportunity to explore the results more fully. An example of how a clinical trial might be reported in the medical literature using these methods is given.

Adrenergic beta-Antagonists↗
PubMed2004

An integrated comprehensive workbench for inferring genetic networks: voyagene.

We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.

Algorithms↗
PubMed2005

A comparison of Bayesian network learning algorithms from continuous data.

Learning a Bayesian network from data is an important problem in biomedicine for the automatic construction of decision support systems and inference of plausible causal relations. Most Bayesian network learning algorithms require discrete data; however discretization may impact the quality of the learned structure. In this project, we present a comparison of different approaches for learning from continuous data to identify the most promising one and to quantify the impact of discretization in Bayesian network learning.

Algorithms↗
PubMedDate not supplied

Bayesian analysis of ROC curves using Markov-chain Monte Carlo methods.

The authors introduce a Bayesian approach to generalized linear regression models for rating data observed in the evaluation of a diagnostic technology. Such models were previously studied using a non-Bayesian approach. In a Bayesian analysis, the difficulties inherent in an ordinal rating scale are circumvented by using data-augmentation techniques. Posterior distributions for the regression parameters- and thereby for receiver operating characteristic (ROC) curve parameters and values, for the area under a ROC curve, differences between areas, etc.-may then be computed by Markov-chain Monte Carlo methods. Inferences are made in standard Bayesian ways. The methods are exemplified by a study of ultrasonography rating data for the detection of hepatic metastases in patients with colon or breast cancer (previously analyzed) and the results compared.

Bayes Theorem↗
PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗
PubMed2006

Phylogenetic relationships of horned lizards (Phrynosoma) based on nuclear and mitochondrial data: evidence for a misleading mitochondrial gene tree.

It has proven remarkably difficult to obtain a well-resolved and strongly supported phylogeny for horned lizards (Phrynosoma) because of incongruence between morphological and mitochondrial DNA sequence data. We infer the phylogenetic relationships among all 17 extant Phrynosoma species using >5.1 kb of mtDNA (12S rRNA, 16S rRNA, ND1, ND2, ND4, Cyt b, and associated tRNA genes), and >2.2kb from three nuclear genes (RAG-1, BDNF, and GAPD) for most taxa. We conduct separate and combined phylogenetic analyses of these data using maximum parsimony, maximum likelihood, and Bayesian methods. The phylogenetic relationships inferred from the mtDNA data are congruent with previous mtDNA analyses based on fewer characters and provide strong support for most branches. However, we detected strong incongruence between the mtDNA and nuclear data using comparisons of branch support and Shimodaira-Hasegawa tests, with the (P. platyrhinos+P. goodei) clade identified as the primary source of this conflict. Our analysis of a P. mcalliixP. goodei hybrid suggests that this incongruence is caused by reticulation via introgressive hybridization. Our preferred phylogeny based on an analysis of the combined data (excluding the introgressed mtDNA data) provides a new framework for interpreting character evolution and biogeography within Phrynosoma. In the context of this improved phylogeny we propose a phylogenetic taxonomy highlighting four clades: (1) Tapaja, containing the viviparous short-horned lizards P. ditmarsi, P. hernandesi, P. douglasii, and P. orbiculare; (2) Anota, containing species with prominent cranial horns (P. solare, P. mcallii, and the P. coronatum group); (3) Doliosaurus, containing three species lacking antipredator blood-squirting (P. modestum, P. platyrhinos, and P. goodei); and (4) Brevicauda, containing two viviparous species with extremely short tails that lack blood-squirting (P. braconnieri and P. taurus).

Animals↗
PubMed2006

Theory-based Bayesian models of inductive learning and reasoning.

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Association Learning↗
PubMed1998

On the use of Bayesian methods for evaluating compartmental neural models.

Computational modeling is being used increasingly in neuroscience. In deriving such models, inference issues such as model selection, model complexity, and model comparison must be addressed constantly. In this article we present briefly the Bayesian approach to inference. Under a simple set of commonsense axioms, there exists essentially a unique way of reasoning under uncertainty by assigning a degree of confidence to any hypothesis or model, given the available data and prior information. Such degrees of confidence must obey all the rules governing probabilities and can be updated accordingly as more data becomes available. While the Bayesian methodology can be applied to any type of model, as an example we outline its use for an important, and increasingly standard, class of models in computational neuroscience--compartmental models of single neurons. Inference issues are particularly relevant for these models: their parameter spaces are typically very large, neurophysiological and neuroanatomical data are still sparse, and probabilistic aspects are often ignored. As a tutorial, we demonstrate the Bayesian approach on a class of one-compartment models with varying numbers of conductances. We then apply Bayesian methods on a compartmental model of a real neuron to determine the optimal amount of noise to add to the model to give it a level of spike time variability comparable to that found in the real cell.

Action Potentials↗
PubMed2003

Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information.

In BOLD fMRI data analysis, robust and accurate estimation of the Hemodynamic Response Function (HRF) is still under investigation. Parametric methods assume the shape of the HRF to be known and constant throughout the brain, whereas non-parametric methods mostly rely on artificially increasing the signal-to-noise ratio. We extend and develop a previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework. A general hypothesis test is also proposed, allowing to take advantage of the knowledge gained regarding the HRF to perform activation detection. The performances of the method are then evaluated by simulation. Great improvement is shown compared to the Maximum-Likelihood estimate in terms of estimation error, variance, and bias. Robustness of the estimators with regard to the actual noise structure or level, as well as the stimulus sequence, is also proven. Lastly, fMRI data with an event-related paradigm are analyzed. As suspected, the regions selected from highly discriminating activation maps resulting from the method exhibit a certain inter-regional homogeneity in term of HRF shape, as well as noticeable inter-regional differences.

Adult↗
PubMed1995

Clinical reasoning about new symptoms despite preexisting disease: sources of error and order effects.

BACKGROUND: Previous work that studied the evaluation of new, atypical symptoms in patients with preexisting diseases indicated that physicians largely ignored the past medical history and therefore erred in their diagnoses, when compared to a Bayesian analysis. Other studies have shown that the order in which information is presented to a decision maker can affect the inferences drawn, again contrary to a Bayesian standard. OBJECTIVES: The aim of the study was to investigate the source of disparity between clinical judgment and Bayesian analysis and to investigate the effect of alternative orders of presenting information on diagnostic conclusions. METHODS: Two groups of family physicians received a written clinical scenario. One group was given the past medical history before the history of present illness, the physical exam, and the laboratory data. The second group learned about the past medical history after all other clinical information had been presented. Judgments of test accuracy and probably diagnosis were collected at several points to identify the source of any diagnostic error. RESULTS: For both groups, the major source of error was in estimating the prior probability of disease, not in estimating the accuracy of a diagnostic test or updating opinions following receipt of test results. Although both groups of physicians received the same information, they came to markedly different conclusions about the most likely diagnosis. The group given the past medical history at the beginning of the scenario considered this information much less significant than did the group who received it at the end. CONCLUSIONS: Family physicians deviate from a Bayesian standard of reasoning by wrongly specifying prior probabilities and by being influenced by the order in which clinical information is presented.

Bayes Theorem↗
PubMed2003

Take the best versus simultaneous feature matching: probabilistic inferences from memory and effects of representation format.

In 4 experiments, the tendency to use the simple heuristic Take The Best (TTB; G. Gigerenzer & D. Goldstein, 1996) was explored for probabilistic multiattribute inferences from memory. In a newly developed procedure, participants first learned attribute patterns that formed the basis for inferences in a second phase. A Bayesian method classified strategies as TTB, compensatory, or guessing. Experiment 1 had a high rate (64%) of participants classified as TTB users when inferences were made from memory. Experiment 2 showed that this was no mere materials effect. In Experiments 3 and 4, the authors examined effects of the representational format of the attribute information. Experiment 4 showed that the representational format may be an important moderating variable for strategy use.

Adult↗