Read by QxMD icon Read


Heejung Shim, Bret Larget
Traditionally, phylogeny and sequence alignment are estimated separately: first estimate a multiple sequence alignment and then infer a phylogeny based on the sequence alignment estimated in the previous step. However, uncertainty in the alignment is ignored, resulting, possibly, in overstated certainty in phylogeny estimates. We develop a joint model for co-estimating phylogeny and sequence alignment which improves estimates from the traditional approach by accounting for uncertainty in the alignment in phylogenetic inferences...
January 18, 2017: Biometrics
Kazem Nasserinejad, Joost van Rosmalen, Wim de Kort, Emmanuel Lesaffre
Identifying the number of classes in Bayesian finite mixture models is a challenging problem. Several criteria have been proposed, such as adaptations of the deviance information criterion, marginal likelihoods, Bayes factors, and reversible jump MCMC techniques. It was recently shown that in overfitted mixture models, the overfitted latent classes will asymptotically become empty under specific conditions for the prior of the class proportions. This result may be used to construct a criterion for finding the true number of latent classes, based on the removal of latent classes that have negligible proportions...
2017: PloS One
Bruce Rannala, Ziheng Yang
We develop a Bayesian method for inferring the species phylogeny under the multispecies coalescent (MSC) model. To improve the mixing properties of the Markov chain Monte Carlo (MCMC) algorithm that traverses the space of species trees, we implement two efficient MCMC proposals: the first is based on the Subtree Pruning and Regrafting (SPR) algorithm and the second is based on a node-slider algorithm. Like the Nearest-Neighbor Interchange (NNI) algorithm we implemented previously, both new algorithms propose changes to the species tree while simultaneously altering the gene trees at multiple genetic loci to automatically avoid conflicts with the newly proposed species tree...
January 4, 2017: Systematic Biology
Eva Santermans, Kim Van Kerckhove, Amin Azmon, W John Edmunds, Philippe Beutels, Christel Faes, Niel Hens
Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand...
December 24, 2016: Mathematical Biosciences
Xavier Meyer, Bastien Chopard, Nicolas Salamin
MOTIVATION: Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods. RESULTS: We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models...
December 24, 2016: Bioinformatics
Yanxun Xu, Peter Müller, Abdus S Wahed, Peter F Thall
We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out...
2016: Journal of the American Statistical Association
Lars Freier, Eric von Lieres
Biotechnological separation processes are routinely designed and optimized using parallel high-throughput experiments and/or serial experiments. Well-characterized processes can further be optimized using mechanistic models. In all these cases - serial/parallel experiments and modeling - iterative strategies are customarily applied for planning novel experiments/simulations based on the previously acquired knowledge. Process optimization is typically complicated by conflicting design targets, such as productivity and yield...
December 23, 2016: Biotechnology Journal
Andrew F Neuwald, Stephen F Altschul
Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly...
December 2016: PLoS Computational Biology
Cornelia Klak, Pavel Hanáček, Peter V Bruyns
The Aizooideae is an early-diverging lineage within the Aizoaceae. It is most diverse in southern Africa, but also has endemic species in Australasia, Eurasia and South America. We derived a phylogenetic hypothesis from Bayesian and Maximum Likelihood analyses of plastid DNA-sequences. We find that one of the seven genera, the fynbos-endemic Acrosanthes, does not belong to the Aizooideae, but is an ancient sister-lineage to the subfamilies Mesembryanthemoideae & Ruschioideae. Galenia and Plinthus are embedded inside Aizoon and Aizoanthemum is polyphyletic...
December 17, 2016: Molecular Phylogenetics and Evolution
Svetlana Bulashevska, Colin Priest, Daniel Speicher, Jörg Zimmermann, Frank Westermann, Armin B Cremers
BACKGROUND: Biological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery...
December 15, 2016: BMC Bioinformatics
Rohan L Fernando, Hao Cheng, Bruce L Golden, Dorian J Garrick
BACKGROUND: Two types of models have been used for single-step genomic prediction and genome-wide association studies that include phenotypes from both genotyped animals and their non-genotyped relatives. The two types are breeding value models (BVM) that fit breeding values explicitly and marker effects models (MEM) that express the breeding values in terms of the effects of observed or imputed genotypes. MEM can accommodate a wider class of analyses, including variable selection or mixture model analyses...
December 8, 2016: Genetics, Selection, Evolution: GSE
Per Sidén, Anders Eklund, David Bolin, Mattias Villani
Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods therefore do inference in subregions of the brain separately or do approximate inference without comparison to the true posterior distribution. A popular such method, which is now the standard method for Bayesian single subject analysis in the SPM software, is introduced in Penny et al. (2005b). The method processes the data slice-by-slice and uses an approximate variational Bayes (VB) estimation algorithm that enforces posterior independence between activity coefficients in different voxels...
November 19, 2016: NeuroImage
Matthew R Borths, Patricia A Holroyd, Erik R Seiffert
Hyaenodonta is a diverse, extinct group of carnivorous mammals that included weasel- to rhinoceros-sized species. The oldest-known hyaenodont fossils are from the middle Paleocene of North Africa and the antiquity of the group in Afro-Arabia led to the hypothesis that it originated there and dispersed to Asia, Europe, and North America. Here we describe two new hyaenodont species based on the oldest hyaenodont cranial specimens known from Afro-Arabia. The material was collected from the latest Eocene Locality 41 (L-41, ∼34 Ma) in the Fayum Depression, Egypt...
2016: PeerJ
Kai Wang, John N Ivan, Nalini Ravishanker, Eric Jackson
In an effort to improve traffic safety, there has been considerable interest in estimating crash prediction models and identifying factors contributing to crashes. To account for crash frequency variations among crash types and severities, crash prediction models have been estimated by type and severity. The univariate crash count models have been used by researchers to estimate crashes by crash type or severity, in which the crash counts by type or severity are assumed to be independent of one another and modelled separately...
November 12, 2016: Accident; Analysis and Prevention
Abhishek Chaudhary, Mohamed M Hantush
Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood estimation (BMCML) to calibrate a lake oxygen recovery model. We first derive an analytical solution of the differential equation governing lake-averaged oxygen dynamics as a function of time-variable wind speed. Statistical inferences on model parameters and predictive uncertainty are then drawn by Bayesian conditioning of the analytical solution on observed daily wind speed and oxygen concentration data obtained from an earlier study during two recovery periods on a eutrophic lake in upper state New York...
January 1, 2017: Water Research
Jianghai Zhao, Hui Zhang, Lunshou Wei, Shuping Xie, Zhimin Suo
A small proportion of hepatocellular carcinoma (HCC) patients are suitable for surgical resections and various minimally invasive procedures have been introduced as alternatives to surgical resections. However, the relative efficacy of minimally invasive procedures remains to be studied in the current literature. Several popular minimally invasive procedures (monotherapy or combined therapies) were selected for comparison and their relative long-term efficacy were determined by using the statistics of hazard ratio (HR) which evaluates the survival status of HCC patients in one, two, three and four years, respectively...
November 7, 2016: Oncotarget
Loic Le Folgoc, Herve Delingette, Antonio Criminisi, Nicholas Ayache
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion...
November 1, 2016: IEEE Transactions on Medical Imaging
Diepreye Ayabina, Charlotte Hendon-Dunn, Joanna Bacon, Caroline Colijn
Drug resistance to tuberculosis (TB) has become more widespread over the past decade. As such, understanding the emergence and fitness of antibiotic-resistant subpopulations is crucial for the development of new interventions. Here we use a simple mathematical model to explain the differences in the response to isoniazid (INH) of Mycobacterium tuberculosis cells cultured under two growth rates in a chemostat. We obtain posterior distributions of model parameters consistent with data using a Markov chain Monte Carlo (MCMC) method...
November 2016: Journal of the Royal Society, Interface
Chun Wang, Gongjun Xu, Zhuoran Shang
Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches...
October 28, 2016: Psychometrika
Ali A Al-Shomrani, A I Shawky, Osama H Arif, Muhammad Aslam
This paper focuses on the application of Markov Chain Monte Carlo (MCMC) technique for estimating the parameters of log-logistic (LL) distribution which is dependent on a complete sample. To find Bayesian estimates for the parameters of the LL model OpenBUGS-established software for Bayesian analysis based on MCMC technique, is employed. It is presumed that samples for independent non informative set of priors for estimating LL parameters are drawn from posterior density function. A proposed module was developed and incorporated in OpenBUGS to estimate the Bayes estimators of the LL distribution...
2016: SpringerPlus
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"