Chris J. Needham

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This paper discusses methods behind tracker evaluation, the aim being to evaluate how well a tracker is able to determine the position of a target object. Few metrics exist for positional tracker evaluation; here the fundamental issues of trajectory comparison are addressed, and metrics are presented which allow the key features to be described. Often(More)
Tracking sports players over a large playing area is a challenging problem. The players move quickly, and have large variations in their silhouettes. This paper presents a framework for multi-object tracking, using a CONDENSATION based approach. Each player being tracked is independently fitted to a model, and the sampling probability for the group of(More)
Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6%(More)
MOTIVATION A number of methods have been reported that predict protein-protein interactions (PPIs) with high accuracy using only simple sequence-based features such as amino acid 3mer content. This is surprising, given that many protein interactions have high specificity that depends on detailed atomic recognition between physiochemically complementary(More)
This paper presents a cognitive vision system capable of autonomously learning protocols from perceptual observations of dynamic scenes. The work is motivated by the aim of creating a synthetic agent that can observe a scene containing interactions between unknown objects and agents, and learn models of these sufficient to act in accordance with the(More)
Bayesian networks (BNs) provide a neat and compact representation for expressing joint probability distributions (JPDs) and for inference. They are becoming increasingly important in the biological sciences for the tasks of inferring cellular networks [1], modelling protein signalling pathways [2], systems biology, data integration [3], classification [4],(More)
Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO-At (gene ontology prediction in A. thaliana), a method that combines five data types (co-expression, sequence, phylogenetic profile, interaction and gene neighbourhood)(More)
MOTIVATION To predict which of the vast number of human single nucleotide polymorphisms (SNPs) are deleterious to gene function or likely to be disease associated is an important problem, and many methods have been reported in the literature. All methods require data sets of mutations classified as 'deleterious' or 'neutral' for training and/or validation.(More)
The elucidation of networks from a compendium of gene expression data is one of the goals of systems biology and can be a valuable source of new hypotheses for experimental researchers. For Arabidopsis, there exist several thousand microarrays which form a valuable resource from which to learn. A novel Bayesian network-based algorithm to infer gene(More)
A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to(More)