Development of new knowledge discovery tools to explore biomedical datasets in breast cancer

The explorative power of high throughput technologies in cancer research has become well established in recent years, exemplified by diverse gene microarray studies. However, development of the necessary biomedical data analysis tools has historically been confined to a commercial environment, while comprehensive, user-friendly analysis approaches are still needed. Availability of freely-available software, notably the 'R' project statistical programming language, allowed development of a user-friendly multivariate statistics application - Informatics Tenovus (I-10) - in this project. I-10 provides a platform through which powerful existing and future 'R' project statistical analysis methodologies can be applied, without prior programming knowledge. The new system was tested in the context of exploring antihormone resistance in breast cancer, analysing microarray datasets from in vitro models of acquired Tamoxifen (TAMR) or Faslodex resistance (FASR) versus endocrine responsive MCF-7 cells. The analysis not only revealed known de-regulated genes, but also further potential future markers/targets for endocrine response/resistance. The advantages of the 'R' programming environment together with Microsoft Visual Basic.net technology for producing user-friendly biomedical analysis tools facilitated subsequent development of a tool which could explore SEER cancer patient datasets. This new cancer query survival tool - Superstes -allows detailed statistical modelling of the impact that multiple patient attributes (in this instance derived from the SEER breast and colorectal cancer datasets) have on patient survival. The versatility of 'R' was additionally demonstrated in further exploring classifiers, where it was able to interface with the sophisticated, freely available machine learning application 'Weka'. Using 'R' and Weka, breast cancer patient survival was modelled using equivalent patient attributes to the Nottingham Prognostic Index and a 10 year survival subset of the SEER breast cancer dataset. Several machine learning methodologies were compared for their ability to accurately model survival, with their value in routine clinical use for prediction of patient survival then critically evaluated.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Brain Tumor Diagnosis Support System: A decision Fusion Framework

An important factor in providing effective and efficient therapy for brain tumors is early and accurate detection, which can increase survival rates. Current image-based tumor detection and diagnosis techniques are heavily dependent on interpretation by neuro-specialists and/or radiologists, making the evaluation process time-consuming and prone to human error and subjectivity. Besides, widespread use of MR spectroscopy requires specialized processing and assessment of the data and obvious and fast show of the results as photos or maps for routine medical interpretative of an exam. Automatic brain tumor detection and classification have the potential to offer greater efficiency and predictions that are more accurate. However, the performance accuracy of automatic detection and classification techniques tends to be dependent on the specific image modality and is well known to vary from technique to technique. For this reason, it would be prudent to examine the variations in the execution of these methods to obtain consistently high levels of achievement accuracy. Designing, implementing, and evaluating categorization software is the goal of the suggested framework for discerning various brain tumor types on magnetic resonance imaging (MRI) using textural features. This thesis introduces a brain tumor detection support system that involves the use of a variety of tumor classifiers. The system is designed as a decision fusion framework that enables these multi-classifier to analyze medical images, such as those obtained from magnetic resonance imaging (MRI). The fusion procedure is ground on the Dempster-Shafer evidence fusion theory. Numerous experimental scenarios have been implemented to validate the efficiency of the proposed framework. Compared with alternative approaches, the outcomes show that the methodology developed in this thesis demonstrates higher accuracy and higher computational efficiency

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

Unsupervised machine learning of high dimensional data for patient stratification

The development mechanisms of numerous complex, rare diseases are largely unknown to scientists partly due to their multifaceted heterogeneity. Stratifying patients is becoming a very important objective as we further research that inherent heterogeneity which can be utilised towards personalised medicine. However, considerable difficulties slow down accurate patient stratification mainly represented by outdated clinical criteria, weak associations or simple symptom categories. Fortunately, immense steps have been taken towards multiple omic data generation and utilisation aiming to produce new insights as in exploratory machine learning which showed the potential to identify the source of disease mechanisms from patient subgroups. This work describes the development of a modular clustering toolkit, named Omada, designed to assist researchers in exploring disease heterogeneity without extensive expertise in the machine learning field. Subsequently, it assesses Omada’s capabilities and validity by testing the toolkit on multiple data modalities from pulmonary hypertension (PH) patients. I first demonstrate the toolkit’s ability to create biologically meaningful subgroups based on whole blood RNA-seq data from H/IPAH patients in the manuscript “Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood”. Our work on the manuscript titled “Diagnostic miRNA signatures for treatable forms of pulmonary hypertension highlight challenges with clinical classification” aimed to apply the same clustering approach on a PH microRNA dataset as a first step in forming microRNA diagnostic signatures by recognising the potential of microRNA expression in identifying diverse disease sub-populations irrespectively of pre-existing PH classes. The toolkit’s effectiveness on metabolite data was also tested. Lastly, a longitudinal clustering approach was explored on activity readouts from wearables on COVID-19 patients as part of our manuscript “Unsupervised machine learning identifies and associates trajectory patterns of COVID-19 symptoms and physical activity measured via a smart watch”. Two clusters of high and low activity trajectories were generated and associated with symptom classes showing a weak but interesting relationship between the two. In summary, this thesis is examining the potential of patient stratification based on several data types from patients that represent a new, unseen picture of disease mechanisms. The tools presented provide important indications of distinct patient groups and could generate the insights needed for further targeted research and clinical associations that can help towards understanding rare, complex diseases