Stock Market Random Forest-Text Mining (SMRF-TM) Approach to Analyse Critical Indicators of Stock Market Movements

The Stock Market is a significant sector of a country’s economy and has a crucial role in the growth of commerce and industry. Hence, discovering efficient ways to analyse and visualise stock market data is considered a significant issue in modern finance. The use of data mining techniques to predict stock market movements has been extensively studied using historical market prices but such approaches are constrained to make assessments within the scope of existing information, and thus they are not able to model any random behaviour of the stock market or identify the causes behind events. One area of limited success in stock market prediction comes from textual data, which is a rich source of information. Analysing textual data related to the Stock Market may provide better understanding of random behaviours of the market. Text Mining combined with the Random Forest algorithm offers a novel approach to the study of critical indicators, which contribute to the prediction of stock market abnormal movements. In this thesis, a Stock Market Random Forest-Text Mining system (SMRF-TM) is developed and is used to mine the critical indicators related to the 2009 Dubai stock market debt standstill. Random forest and expectation maximisation are applied to classify the extracted features into a set of meaningful and semantic classes, thus extending current approaches from three to eight classes: critical down, down, neutral, up, critical up, economic, social and political. The study demonstrates that Random Forest has outperformed other classifiers and has achieved the best accuracy in classifying the bigram features extracted from the corpus

Expanding the toolbox for the study of antimicrobial peptides

There is an urgent lack of new antibiotics in the face of an ever-expanding antimicrobial resistance crisis. The fact that fewer new classes of antibiotics are being developed, and resistance soon follows newly available antibiotics, only serves to underline the urgency of the matter. There is a clear need of a paradigm shift with regards to antibiotics, and one such hope is antimicrobial peptides (AMPs). AMPs are an integral part of the innate immune systems of most organisms within the domains of life; since their discovery they have become of significant interest as a new type of antimicrobial agent, due in part to the low capacity of bacteria to develop resistance mechanisms towards them. Despite their potential, and lengthy study so far, establishing the specifics of the mechanism of action of many AMPs remains difficult– particularly of those that target the bacterial cell membrane. This lack of understanding limits the ability to rationally design new AMPs with a view to developing new antimicrobial agents. The aim of this work was to help identify new potential hit compounds through NMR structure elucidation, and to develop new methods that would give greater insight into the activity of membrane active AMPs. This in turn could help enable the rational design of new AMPs. WIND-PVPA, a method to quantify permeabilities of water and ions as a means to evaluate the disruptive capabilities of AMPs, was developed. This was demonstrated on a number of AMPs, and it was shown that WIND-PVPA can identify AMPs that have strong, selective, membrane disruptive activities such as the AMP WRWRWR, as well as more modestly disruptive AMPs such as KP-76. The WIND-PVPA was further used with a non-AMP membrane active natural product – lulworthinone – that was characterised over the course of the project. The findings of the study helped classify lulworthinone as a non-disruptive membrane active agent. In addition, microscale thermophoresis (MST) was shown to be a viable method by which the binding and partition coefficients of Trp-rich AMPs can be determined, and it was shown that the derived lipid-bindings of the AMPs correlates well with their bactericidal activity. Both WIND-PVPA and MST have expanded the toolbox available to the study of AMP-lipid interactions and can be used synergistically to give greater insight into the possible mechanism by which AMPs act, by helping to identify interesting cases, such as non-disruptive AMPs with potent activities. In summary, the methods developed have great potential that can be further refined into robust methods that can greatly assist in the deconvolution of AMP activity and can open up possibilities of the rational design of membrane active AMPs as a new generation of antimicrobial agents.

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas

Analytical and computational methods towards a metabolic model of ageing in Caenorhabditis elegans

Human life expectancy is increasing globally. This has major socioeconomic implications, but also raises scientific questions about the biological bases of ageing and longevity. Research on appropriate model organisms, such as the nematode worm Caenorhabditis elegans, is a key component of answering these questions. Ageing is a complex phenomenon, with both environmental and genetic influences. Metabolomics, the analysis of all small molecules within a biological system, offers the ability to integrate these complex factors to help understand the role of metabolism in ageing. This thesis addresses the current lack of methods for C. elegans metabolite analysis, with a particular focus on combining analytical and computational approaches. As a first essential step, C. elegans metabolite extraction protocols for NMR, GC-MS and LC-MS based analysis were optimized. Several methods to improve the coverage, automatic annotation and data analysis steps of NMR and GC-MS are proposed. Next, stable isotope labelling was explored as a tool for C. elegans metabolomics. An automated stable isotope based workflow was developed, which identifies all biological, non-redundant features within a LC-MS acquisition and annotates them with molecular compositions. This demonstrated that the vast majority (> 99.5%) of detected features inside LC-MS metabolomics experiments are not of biological origin or redundant. This stable isotope workflow was then used to compare the metabolism of 24 different C. elegans mutant strains from different pathways (e.g. insulin signalling, TOR pathway, neuronal signalling), with differing levels of lifespan extension compared to wild-type worms. The biologically relevant features (metabolites) were detected and annotated, and compared across the mutants. Some metabolites were correlated with longevity across the mutant set, in particular, glycerophospholipids. This led to the formulation of a hypothesis, that lifespan extension in C. elegans requires increased activity of common downstream longevity effector mechanisms (autophagy, and mitochondrial biogenesis), that also involve subcellular compartmentation and hence membrane formation. This results in the alterations in lipid metabolism detected here.Open Acces