David Bulger

Parameter estimation for robust HMM analysis of ChIP-chip data

BackgroundTiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis.ResultsHere we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure.ConclusionWe illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.

Grover's Quantum Algorithm Applied to Global Optimization

Grovers quantum computational search procedure can provide the basis for implementing adaptive global optimization algorithms. A brief overview of the procedure is given and a framework called Grover adaptive search is set up. A method of Durr and Hoyer and one introduced by the authors fit into this framework and are compared.

Trends and Weekly and Seasonal Cycles in the Rate of Errors in the Clinical Management of Hospitalized Patients

Studies on the rate of adverse events in hospitalized patients seldom examine temporal patterns. This study presents evidence of both weekly and annual cycles. The study is based on a large and diverse data set, with nearly 5 yrs of data from a voluntary staff-incident reporting system of a large public health care provider in rural southeastern Australia. The data of 63 health care facilities were included, ranging from large non-metropolitan hospitals to small community and aged health care facilities. Poisson regression incorporating an observation-driven autoregressive effect using the GLARMA framework was used to explain daily error counts with respect to long-term trend and weekly and annual effects, with procedural volume as an offset. The annual pattern was modeled using a first-order sinusoidal effect. The rate of errors reported demonstrated an increasing annual trend of 13.4% (95% confidence interval [CI] 10.6% to 16.3%); however, this trend was only significant for errors of minor or no harm to the patient. A strong “weekend effect” was observed. The incident rate ratio for the weekend versus weekdays was 2.74 (95% CI 2.55 to 2.93). The weekly pattern was consistent for incidents of all levels of severity, but it was more pronounced for less severe incidents. There was an annual cycle in the rate of incidents, the number of incidents peaking in October, on the 282nd day of the year (spring in Australia), with an incident rate ratio 1.09 (95% CI 1.05 to 1.14) compared to the annual mean. There was no so-called “killing season” or “July effect,” as the peak in incident rate was not related to the commencement of work by new medical school graduates. The major finding of this study is the rate of adverse events is greater on weekends and during spring. The annual pattern appears to be unrelated to the commencement of new graduates and potentially results from seasonal variation in the case mix of patients or the health of the medical workforce that alters health care performance. These mechanisms will need to be elucidated with further research. (Author correspondence: david.buckley@gsahs.health.nsw.gov.au)

ChIPseqR: analysis of ChIP-seq experiments

BackgroundThe use of high-throughput sequencing in combination with chromatin immunoprecipitation (ChIP-seq) has enabled the study of genome-wide protein binding at high resolution. While the amount of data generated from such experiments is steadily increasing, the methods available for their analysis remain limited. Although several algorithms for the analysis of ChIP-seq data have been published they focus almost exclusively on transcription factor studies and are usually not well suited for the analysis of other types of experiments.ResultsHere we present ChIPseqR, an algorithm for the analysis of nucleosome positioning and histone modification ChIP-seq experiments. The performance of this novel method is studied on short read sequencing data of Arabidopsis thaliana mononucleosomes as well as on simulated data.ConclusionsChIPseqR is shown to improve sensitivity and spatial resolution over existing methods while maintaining high specificity. Further analysis of predicted nucleosomes reveals characteristic patterns in nucleosome sequences and placement.

Stopping and restarting strategy for stochastic sequential search in global optimization

Two common questions when one uses a stochastic global optimization algorithm, e.g., simulated annealing, are when to stop a single run of the algorithm, and whether to restart with a new run or terminate the entire algorithm. In this paper, we develop a stopping and restarting strategy that considers tradeoffs between the computational effort and the probability of obtaining the global optimum. The analysis is based on a stochastic process called Hesitant Adaptive Search with Power-Law Improvement Distribution (HASPLID). HASPLID models the behavior of stochastic optimization algorithms, and motivates an implementable framework, Dynamic Multistart Sequential Search (DMSS). We demonstrate here the practicality of DMSS by using it to govern the application of a simple local search heuristic on three test problems from the global optimization literature.

Approximate Implementations of Pure Random Search in the Presence of Noise

We discuss the noisy optimisation problem, in which function evaluations are subject to random noise. Adaptation of pure random search to noisy optimisation by repeated sampling is considered. We introduce and exploit an improving bias condition on noise-affected pure random search algorithms. Two such algorithms are considered; we show that one requires infinite expected work to proceed, while the other is practical.

Estimation of the reproductive number for the 2009 pandemic H1N1 influenza in rural and metropolitan New South Wales

OBJECTIVE During the early stages of pandemics, when resource planning occurs, the epidemiological parameters of the agent are often poorly described. These estimates are typically derived from metropolitan centres. This paper examines the spread of the 2009 pandemic H1N1 virus in rural and regional New South Wales compared with metropolitan centres. DESIGN Retrospective statistical analysis of longitudinal data. SETTING Ecological examination of spread of influenza in the general community of New South Wales, Australia. PARTICIPANTS Number of notified infections with novel pandemic H1N1 influenza in rural/regional (n=241) and metropolitan (n=1788) health service areas of New South Wales during the period 1 June 2009 and 12 July 2009. MAIN OUTCOME MEASURES   A comparison of the reproductive number for the 2009 pandemic H1N1 in rural/regional and metropolitan New South Wales. RESULTS The reproductive number of the pandemic H1N1 in rural New South Wales was 1.26 (95% confidence interval (CI), 1.22-1.30) compared with estimates of metropolitan New South Wales of 1.28 (95% CI, 1.26-1.30). This difference was not statistically significant (P=0.314). These estimates are lower than those previously published and of the order of magnitude typically observed with seasonal flu. This was consistent with the clinical observations in Greater Southern Area Health Service. CONCLUSION The apparent invariance in the rate of spread of influenza between rural and metropolitan areas should provide rural health care providers with confidence in metropolitan derived estimates when planning in future influenza pandemics.

Position-dependent and cooperative quantum Parrondo walks

This work is a study on quantum computational formulations of Parrondo walks, that is, positively trending random walks formed as combinations of negatively trending random walks. We reanalyse the position-dependent walk proposed by Ko??k et al (2007 J. Mod. Opt.?54 2275), correcting the parameter choices in that paper to achieve the Parrondo effect. We also devise a quantum analogue of the cooperative Parrondo walk of Toral (2002 Fluct. Noise Lett.?2 L305), in which it is the interaction between multiple participants, rather than position-dependence, that allows the Parrondo effect to occur. We give a general formulation of a quantum analogue of the classical walk of Toral (2002 Fluct. Noise Lett.?2 L305), and demonstrate the Parrondo effect numerically. Lastly, we highlight a qualitative difference in asymptotic behaviour between quantum Parrondo walks and their classical counterparts. In particular, we draw attention to an intuitive but unreliable assumption, based on classical random walks, which may pose extra challenges for applications of the Parrondo effect in the quantum setting seeking to separate or classify data or particles.

Generating functions and the performance of backtracking adaptive search

Backtracking adaptive search is a simplified stochastic optimisation procedure which permits the acceptance of worsening objective function values. Key properties of backtracking adaptive search are defined and obtained using generating functions. Examples are given to illustrate the use of this methodology.

Expected hitting times for backtracking adaptive search

This article analyses a counting process associated with a stochastic process arising in global optimisation. Backtracking adaptive search (BAS) is a theoretical stochastic global optimisation algorithm modelling the temporary acceptance of solutions of lower quality. BAS generalises the pure adaptive search and hesitant adaptive search algorithms, whose full search duration distributions are known. This article gives the exact expected search duration for backtracking adaptive search.