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Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector 1 Anopheles. Of particular interest is the molecular biology underlying the immune response system of Anopheles which actively… (More)

We introduce a procedure for generalized monotonic curve fitting that is based on a Bayesian analysis of the isotonic regression model. Conventional isotonic regression fits monotonically increasing step functions to data. In our approach we treat the number and location of the steps as random. For each step level we adopt the conjugate prior to the… (More)

We present a method for Bayesian model-based hierarchical coclustering of gene expression data and use it to study the temporal transcription responses of an Anopheles gambiae cell line upon challenge with multiple microbial elicitors. The method fits statistical regression models to the gene expression time series for each experiment and performs… (More)

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus… (More)

BACKGROUND
Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in… (More)

Single-molecule localization-based super-resolution microscopy techniques such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) produce pointillist data sets of molecular coordinates. Although many algorithms exist for the identification and localization of molecules from raw image data, methods for… (More)

The network traffic generated by a computer, or a pair of computers, is often well modelled as a series of sessions. These are, roughly speaking, intervals of time during which a computer is engaging in the same, continued, activity. This article explores a variety of statistical approaches to re-discovering sessions from network flow data using timing… (More)

How can we effectively use costly statistical models in the defence of large computer networks? Statistical modelling and machine learning are potentially powerful ways to detect threats as they do not require a human level understanding of the attack. However, they are rarely applied in practice as the computational cost of deploying all but the most… (More)

We present a method for Bayesian model-based hierarchical co-clustering of gene expression data and use it to study the temporal transcription responses of an Anopheles gambiae cell line upon challenge with multiple microbial elicitors. The method fits statistical regression models to the gene expression time series for each experiment, and performs… (More)