Global regulatory, manufacturing and consumer trends are driving a need for change in
current pharmaceutical sector business models, with a specific focus on the inherently …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn …

Polymer mechanochemistry is an exciting, rapidly developing field; however, it lacks detailed
studies on the link between lab-scale mechanical activation and how the chemistry will …

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper
proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It …

Motivation We recently published MS2LDA, a method for the decomposition of sets of molecular
fragment data derived from large metabolomics experiments. To make the method more …

The formation of complex and functional molecular structures triggered by the use of small
templates and selfassembly processes is central to polymer and supramolecular chemistry, …

Specialised metabolites from microbial sources are well-known for their wide range of
biomedical applications, particularly as antibiotics. When mining paired genomic and …

Motivation: The use of liquid chromatography coupled to mass spectrometry has enabled the
high-throughput profiling of the metabolite composition of biological samples. However, the …

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted
metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based …

Changes in the plasma metabolic profile were characterised in newly diagnosed rheumatoid
arthritis (RA) patients upon commencement of conventional disease-modifying anti-…