Computational Aspects of Optional Polya Tree.
Ontology highlight
ABSTRACT:
SUBMITTER: Jiang H
PROVIDER: S-EPMC4874344 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
Computational Aspects of Optional Pólya Tree.
Jiang Hui H Mu John Chong JC Yang Kun K Du Chao C Lu Luo L Wong Wing Hung WH
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20160309 1
Optional Pólya tree (OPT) is a flexible nonparametric Bayesian prior for density estimation. Despite its merits, the computation for OPT inference is challenging. In this paper we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named limited-lookahead optional Pólya tree (LL-OPT), aims at accelerating the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewis ...[more]
Similar Datasets
Project description:Analyzing the failure times of multiple events is of interest in many fields. Estimating the joint distribution of the failure times in a non-parametric way is not straightforward because some failure times are often right-censored and only known to be greater than observed follow-up times. Although it has been studied, there is no universally optimal solution for this problem. It is still challenging and important to provide alternatives that may be more suitable than existing ones in specific settings. Related problems of the existing methods are not only limited to infeasible computations, but also include the lack of optimality and possible non-monotonicity of the estimated survival function. In this paper, we proposed a non-parametric Bayesian approach for directly estimating the density function of multivariate survival times, where the prior is constructed based on the optional Pólya tree. We investigated several theoretical aspects of the procedure and derived an efficient iterative algorithm for implementing the Bayesian procedure. The empirical performance of the method was examined via extensive simulation studies. Finally, we presented a detailed analysis using the proposed method on the relationship among organ recovery times in severely injured patients. From the analysis, we suggested interesting medical information that can be further pursued in clinics.
| S-EPMC3862208 | biostudies-literature
Project description:We present a simple, efficient, and computationally cheap sampling method for exploring an un-normalized multivariate density on ?(d), such as a posterior density, called the Polya tree sampler. The algorithm constructs an independent proposal based on an approximation of the target density. The approximation is built from a set of (initial) support points - data that act as parameters for the approximation - and the predictive density of a finite multivariate Polya tree. In an initial "warming-up" phase, the support points are iteratively relocated to regions of higher support under the target distribution to minimize the distance between the target distribution and the Polya tree predictive distribution. In the "sampling" phase, samples from the final approximating mixture of finite Polya trees are used as candidates which are accepted with a standard Metropolis-Hastings acceptance probability. Several illustrations are presented, including comparisons of the proposed approach to Metropolis-within-Gibbs and delayed rejection adaptive Metropolis algorithm.
| S-EPMC3226339 | biostudies-literature
Project description:The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60, 105-115). We explain and exploit the equivalence of N-mixture and multivariate Poisson and negative-binomial models, which provides powerful new approaches for fitting these models. We show that particularly when detection probability and the number of sampling occasions are small, infinite estimates of abundance can arise. We propose a sample covariance as a diagnostic for this event, and demonstrate its good performance in the Poisson case. Infinite estimates may be missed in practice, due to numerical optimization procedures terminating at arbitrarily large values. It is shown that the use of a bound, K, for an infinite summation in the N-mixture likelihood can result in underestimation of abundance, so that default values of K in computer packages should be avoided. Instead we propose a simple automatic way to choose K. The methods are illustrated by analysis of data on Hermann's tortoise Testudo hermanni.
| S-EPMC4406156 | biostudies-literature
Project description:The concept of the hypothesis-driven or observational-based expansion of the therapeutic application of drugs is very seductive. This is due to a number of factors, such as lower cost of development, higher probability of success, near-term clinical potential, patient and societal benefit, and also the ability to apply the approach to rare, orphan, and underresearched diseases. Another highly attractive aspect is that the "barrier to entry" is low, at least in comparison to a full drug discovery operation. The availability of high-performance computing, and databases of various forms have also enhanced the ability to pose reasonable and testable hypotheses for drug repurposing, rescue, and repositioning. In this article we discuss several factors that are currently underdeveloped, or could benefit from clearer definition in articles presenting such work. We propose a classification scheme-drug repositioning evidence level (DREL)-for all drug repositioning projects, according to the level of scientific evidence. DREL ranges from zero, which refers to predictions that lack any experimental support, to four, which refers to drugs approved for the new indication. We also present a set of simple concepts that can allow rapid and effective filtering of hypotheses, leading to a focus on those that are most likely to lead to practical safe applications of an existing drug. Some promising repurposing leads for malaria (DREL-1) and amoebic dysentery (DREL-2) are discussed.
| S-EPMC4533090 | biostudies-other
Project description:Understanding human sleep requires appropriate animal models. Sleep has been extensively studied in rodents, although rodent sleep differs substantially from human sleep. Here we investigate sleep in tree shrews, small diurnal mammals phylogenetically close to primates, and compare it to sleep in rats and humans using electrophysiological recordings from frontal cortex of each species. Tree shrews exhibited consolidated sleep, with a sleep bout duration parameter, τ, uncharacteristically high for a small mammal, and differing substantially from the sleep of rodents that is often punctuated by wakefulness. Two NREM sleep stages were observed in tree shrews: NREM, characterized by high delta waves and spindles, and an intermediate stage (IS-NREM) occurring on NREM to REM transitions and consisting of intermediate delta waves with concomitant theta-alpha activity. While IS-NREM activity was reliable in tree shrews, we could also detect it in human EEG data, on a subset of transitions. Finally, coupling events between sleep spindles and slow waves clustered near the beginning of the sleep period in tree shrews, paralleling humans, whereas they were more evenly distributed in rats. Our results suggest considerable homology of sleep structure between humans and tree shrews despite the large difference in body mass between these species.
| S-EPMC8196209 | biostudies-literature
Project description:BackgroundVaccine pharmacovigilance relates to the detection of adverse events, their assessment, understanding, and prevention, and communication of their risk to the public. These activities can be tedious and long lasting for regulatory authority scientists and may be affected by community practices and public health policies. To better understand underlying challenges, we examined vaccine adverse event reports, assessed whether data-driven techniques can provide additional insight in safety characterization, and wondered on the impact of socioeconomic parameters.MethodsFirst, we integrated VAERS content with additional sources of drug and molecular data and examined reaction and outcome occurrence by using disproportionality metrics and enrichment analysis. Second, we reviewed social and behavioral determinants that may affect vaccine pharmacovigilance aspects.ResultsWe describe our experience in processing more than 607000 vaccine adverse event reports and report on the challenges to integrate more than 95500 VAERS medication narratives with structured information about drugs and other therapeutics or supplements. We found that only 12.6% of events were serious, while 8.97% referred to polypharmacy cases. Exacerbation of serious clinical patient outcomes was observed in 8.88% VAERS cases in which drugs may interact with vaccinations or with each other, regardless of vaccine activity interference. Furthermore, we characterized the symptoms reported in those cases and summarized reaction occurrence among vaccine-types. Last, we examine socioeconomic parameters and cost-management features, explore adverse event reporting trends, and highlight perspectives relating to the use and development of digital services, especially in the context of personalized and collaborative health-care.ConclusionsThis work provides an informative review of VAERS, identifies challenges and limitations in the processing of vaccine adverse event data, and calls for the better understanding of the socioeconomic landscape pertaining vaccine safety concerns. We expect that adoption of computational techniques for integrated safety assessment and interpretation is key not only to pharmacovigilance practice but also to stakeholders from the entire healthcare system.
| S-EPMC6399563 | biostudies-literature
Project description:The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1,000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individual's disease outcomes and treatment options. Systems approaches including mathematical modeling can aid importantly in understanding the multifactorial liver disease etiology leading toward tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present LiverSex, the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on in silico liver model SteatoNet and the object-oriented modeling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced in silico with the uniform model SteatoNet. LiverSex represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.
| S-EPMC5907313 | biostudies-literature
Project description:Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.
| S-EPMC6269008 | biostudies-other
Project description:To study a core component of human intelligence-our ability to combine the meaning of words-neuroscientists have looked to linguistics. However, linguistic theories are insufficient to account for all brain responses reflecting linguistic composition. In contrast, we adopt a data-driven approach to study the composed meaning of words beyond their individual meaning, which we term 'supra-word meaning'. We construct a computational representation for supra-word meaning and study its brain basis through brain recordings from two complementary imaging modalities. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests that composed meaning might be maintained through a different neural mechanism than the synchronized firing of pyramidal cells. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.
| S-EPMC9912822 | biostudies-literature
Project description:BackgroundCo-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions and to predict physical interactions. More generally, it can be used for answering fundamental questions about the evolution of biological systems.Orthologs that exhibit a strong signal of co-evolution in a certain part of the evolutionary tree may show a mild signal of co-evolution in other branches of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive selection. Previous publications in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.ResultsIn this work, we describe a new set of biological problems that are related to finding patterns of local co-evolution. We discuss their computational complexity and design algorithms for solving them. These algorithms outperform other bi-clustering methods as they are designed specifically for solving the set of problems mentioned above.We use our approach to trace the co-evolution of fungal, eukaryotic, and mammalian genes at high resolution across the different parts of the corresponding phylogenetic trees. Specifically, we discover regions in the fungi tree that are enriched with positive evolution. We show that metabolic genes exhibit a remarkable level of co-evolution and different patterns of co-evolution in various biological datasets.In addition, we find that protein complexes that are related to gene expression exhibit non-homogenous levels of co-evolution across different parts of the fungi evolutionary line. In the case of mammalian evolution, signaling pathways that are related to neurotransmission exhibit a relatively higher level of co-evolution along the primate subtree.ConclusionsWe show that finding local patterns of co-evolution is a computationally challenging task and we offer novel algorithms that allow us to solve this problem, thus opening a new approach for analyzing the evolution of biological systems.
| S-EPMC3224649 | biostudies-literature