A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data

In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.

Deciding between theories of how reasoning develops is hard

Although Sloutsky agrees with our interpretation of our data, he argues that the totality of the evidence supports his claim that children make inductive generalisations on the basis of similarity. Here we take issue with his characterisation of the alternative hypotheses in his informal analysis of the data, and suggest that a thorough Bayesian analysis, although practically very difficult, is likely to result in a more finely balanced outcome than he suggests. (c) 2008 Elsevier B.V. All rights reserved.

The vegetation and climate history of the last glacial cycle in a new pollen record from Lake Fimon (southern Alpine foreland, N-Italy)

The sediments of Like Fimon N Italy contain the first continuous archive of the Late Pleistocene environmental and climate history of the southern Alpine foreland We present here the detailed palynological record of the interval between Termination II and the List Glacial Maximum The age-depth model is obtained by radiocarbon dating in the uppermost part of the record Downward we con elated major forest expansion and contraction events to isotopic events in the Greenland Ice core records via a stepping-stone approach involving intermediate correlation to isotopic events dated by TIMS U/Th in Alpine and Apennine stalagmites and to pollen records from mime cores of the Iberian margin Modelled ages obtained by Bayesian analysis of deposition are thoroughly consistent with actual ages with maximum offset of +/- 1700 years Sharp expansion of broad-leaved temperate forest and of sudden water table rise mark the onset of the Last Interglacial after a treeless steppe phase at the end of penultimate glaciation This event is actually a two-step process which matches the two step rise observed in the isotopic record of the nearby Antro del Corchia stalagmite respectively dated to 132 5 +/- 2 5 and 129 +/- 1 5 ka At the interglacial decline mixed oak forests were replaced by oceanic mixed forests the latter persisting further for 7 ka till the end of the Eemian succession Warm-temperate woody species are still abundant at the Eemian end corroborating a steep gradient between central Europe and the Alpine divide at the inception of the last glacial After a stadial phase marked by moderate forest decline a new expansion of warm broad leaved forests interrupted by minor events and followed by mixed oceanic forests can be identified with the north-alpine Saint Germain I The spread of beech during the oceanic phase is a valuable circumalpine marker The subsequent stadial-interstadial succession lacking the telocratic oceanic phase is also consistent with the evidence at the north alpine foreland The Middle Wurmian (full glacial) is marked by persistence of mixed forests dominated by conifers but with significant lime and other broad leaved species A major Arboreal Pollen decrease is observed at modelled age of 38 7 +/- 0 5 ka (larch expansion and last occurrence of lime) which his been related to Heinrich Event 4 The evidence of afforestation persisting south of the Alps throughout most of MIS 3 contrasts with a boreal and continental landscape known for the northern alpine foreland pointing to a sharp rainfall boundary at the Alpine divide and to southern air circulation This is in agreement with the Alpine paleoglaciological record and is supported by the pressure and rainfall patterns designed by mesoscale paleoclimate simulations Strenghtening the continental high pressure during the full glacial triggered cyclogenesis in the middle latitude eastern Europe and orographic rainfall in the eastern Alps and the Balkanic mountains thus allowing forests development at current sea level altitudes (C) 2010 Elsevier Ltd All rights reserved

Flexible paleoclimate age-depth models using an autoregressive gamma process

Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called “Bacon”) that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

Higher incidence of childhood-onset type 1 diabetes mellitus in remote areas: a UK regional small-area analysis

Aims/hypothesis: We investigated the association between the incidence of type 1 diabetes mellitus and remoteness (a proxy measure for exposure to infections) using recently developed techniques for statistical analysis of small-area data.

Subjects, materials and methods: New cases in children aged 0 to 14 years in Northern Ireland were prospectively registered from 1989 to 2003. Ecological analysis was conducted using small geographical units (582 electoral wards) and area characteristics including remoteness, deprivation and child population density. Analysis was conducted using Poisson regression models and Bayesian
hierarchical models to allow for spatially correlated risks that were potentially caused by unmeasured explanatory variables.

Results: In Northern Ireland between 1989 and 2003, there were 1,433 new cases of type 1 diabetes, giving a directly standardised incidence rate of 24.7 per 100,000 personyears. Areas in the most remote fifth of all areas had a significantly (p=0.0006) higher incidence of type 1 diabetes mellitus (incidence rate ratio=1.27 [95% CI 1.07, 1.50]) than those in the most accessible fifth of all areas. There was also a higher incidence rate in areas that were less deprived (p<0.0001) and less densely populated (p=0.002). After adjustment for deprivation and additional adjustment for child population density the association between diabetes and remoteness remained significant (p=0.01 and p=0.03, respectively).

Conclusions/interpretation: In Northern Ireland, there is evidence that remote areas experience higher rates of type 1 diabetes mellitus. This could reflect a reduced or delayed exposure to infections, particularly early in life, in these areas.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

High precision C-14 dating of Holocene peat deposits: A comparison of Bayesian calibration and wiggle-matching approaches

The chronologies of five northern European ombrotrophic peat bogs subjected to a large ANIS C-14 dating effort (32-44 dates/site) are presented here. The results of Bayesian calibration (BCal) of dates with a prior assumption of chronological ordering were compared with a Bayesian wiggle-match approach (Bpeat) which assumes constant linear accumulation over sections of the peat profile. Interpolation of BCal age estimates of dense sequences of C-14 dates showed variable patterns of peat accumulation with time, with changes in accumulation occurring at intervals ranging from 20 to 50 cm. Within these intervals, peat accumulation appeared to be relatively linear. Close analysis suggests that some of the inferred variations in accumulation rate were related to the plant macrofossil composition of the peat. The wiggle-matched age-depth models had relatively high chronological uncertainty within intervals of closely spaced 14 C dates, suggesting that the premise of constant linear accumulation over large sections of the peat profile is unrealistic. Age models based on the assumption of linear accumulation over large parts of a peat core (and therefore only effective over millennial timescales), are not compatible with studies examining environmental change during the Holocene, where variability often occurs at decadal to centennial time-scales. Ideally, future wiggle-match age models should be constrained, with boundaries between sections based on the plant macrofossil composition of the peat and physical-chemical parameters such as the degree of decomposition. Strategies for the selection of material for dating should be designed so that there should be enough C-14 dates to accurately reconstruct the peat accumulation rate of each homogeneous stratigraphic unit. (c) 2006 Elsevier Ltd. All rights reserved.

Phylogeographic analysis of North American populations of the parasitic herbaceous plant Monotropa hypopitys reveals a complex history of range expansion from multiple late glacial refugia

Aim We carried out a phylogeographic study across the range of the herbaceous plant species Monotropa hypopitys L. in North America to determine whether its current disjunct distribution is due to recolonization from separate eastern and western refugia after the Last Glacial Maximum (LGM). Location North America: Pacific Northwest and north-eastern USA/south-eastern Canada. Methods Palaeodistribution modelling was carried out to determine suitable climatic regions for M. hypopitys at the LGM. We analysed between 155 and 176 individuals from 39 locations spanning the species' entire range in North America. Sequence data were obtained for the chloroplast rps2 gene (n=168) and for the nuclear ITS region (n=158). Individuals were also genotyped for eight microsatellite loci (n=176). Interpolation of diversity values was used to visualize the range-wide distribution of genetic diversity for each of the three marker classes. Minimum spanning networks were constructed showing the relationships between the rps2 and ITS haplotypes, and the geographical distributions of these haplotypes were plotted. The numbers of genetic clusters based on the microsatellite data were estimated using Bayesian clustering approaches. Results The palaeodistribution modelling indicated suitable climate envelopes for M. hypopitys at the LGM in both the Pacific Northwest and south-eastern USA. High levels of genetic diversity and endemic haplotypes were found in Oregon, the Alexander Archipelago, Wisconsin, and in the south-eastern part of the species' distribution range. Main conclusions Our results suggest a complex recolonization history for M. hypopitys in North America, involving persistence in separate eastern and western refugia. A generally high degree of congruence between the different marker classes analysed indicated the presence of multiple refugia, with at least two refugia in each area. In the west, putative refugia were identified in Oregon and the Alexander Archipelago, whereas eastern refugia may have been located in the southern part of the species' current distribution, as well as in the 'Driftless Area'. These findings are in contrast to a previous study on the related species Orthilia secunda, which has a similar disjunct distribution to M. hypopitys, but which appears to have recolonized solely from western refugia. © 2011 Blackwell Publishing Ltd.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.