All Outputs (113)

Personalising mobile advertising based on users’ installed apps (2014)
Conference Proceeding

Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule min... Read More about Personalising mobile advertising based on users’ installed apps.

Practical detection of a definitive biomarker panel for Alzheimer's disease: comparisons between matched plasma and cerebrospinal fluid (2014)
Journal Article

Previous mass spectrometry analysis of cerebrospinal fluid (CSF) has allowed the identification of a panel of molecular markers that are associated with Alzheimer’s disease (AD). The panel comprises Amyloid beta, Apolipoprotein E, Fibrinogen alpha ch... Read More about Practical detection of a definitive biomarker panel for Alzheimer's disease: comparisons between matched plasma and cerebrospinal fluid.

Comparison of algorithms that detect drug side effects using electronic healthcare databases (2013)
Journal Article

The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare dat... Read More about Comparison of algorithms that detect drug side effects using electronic healthcare databases.

A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery (2013)
Journal Article

Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a... Read More about A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery.

A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means (2013)
Journal Article

Previously, a semi-manual method was used to identify six novel and clinically useful classes in the Nottingham Tenovus Breast Cancer dataset. 663 out of 1,076 patients were classified. The objectives of our work is three folds. Firstly, our primary... Read More about A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means.

A quantifier-based fuzzy classification system for breast cancer patients (2013)
Journal Article

Objectives:Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms ha... Read More about A quantifier-based fuzzy classification system for breast cancer patients.

Towards a more systematic approach to secure systems design and analysis (2013)
Journal Article

The task of designing secure software systems is fraught with uncertainty, as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, experts may interpret the security ri... Read More about Towards a more systematic approach to secure systems design and analysis.

Attributes for causal inference in electronic healthcare databases (2013)
Conference Proceeding

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria... Read More about Attributes for causal inference in electronic healthcare databases.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data (2012)
Journal Article

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new... Read More about Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Discovering sequential patterns in a UK general practice database (2012)
Conference Proceeding

The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowin... Read More about Discovering sequential patterns in a UK general practice database.

An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control (2012)
Conference Proceeding

It has often been suggested that when faced with large amount of uncertainty in situations of automated control type-2 fuzzy logic based controllers will out perform the simpler type-1 varieties due to the latter lacking a mechanism to model this un... Read More about An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control.

MysiRNA-designer: a workflow for efficient siRNA design (2011)
Journal Article

The design of small interfering RNA (siRNA) is a multi factorial problem that has gained the attention of many researchers in the area of therapeutic and functional genomics. MysiRNA score was previously introduced that improves the correlation of si... Read More about MysiRNA-designer: a workflow for efficient siRNA design.

A "non-parametric" version of the naive Bayes classifier (2011)
Journal Article

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence... Read More about A "non-parametric" version of the naive Bayes classifier.

Learning pathway-based decision rules to classify microarray cancer samples (2010)
Conference Proceeding

Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninformative genes. Combining microarray data with cellular pathway data by us... Read More about Learning pathway-based decision rules to classify microarray cancer samples.

A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients (2010)
Journal Article

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’... Read More about A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.

Consensus clustering and fuzzy classification for breast cancer prognosis (2010)
Conference Proceeding

A novel framework to elucidate core classes in a dataset (2010)
Conference Proceeding

In this paper we present an original framework to extract representative groups from a dataset, and we validate it over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visu... Read More about A novel framework to elucidate core classes in a dataset.

Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms (2010)
Journal Article

In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms. This paper demonstrates that a set of behavi... Read More about Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms.

Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems (2009)
Presentation / Conference

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a... Read More about Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems.

Cancer profiles by Affinity Propagation (2009)
Journal Article

The Affinity Propagation algorithm is applied to various problems of breast and cutaneous tumours subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. Well know breast... Read More about Cancer profiles by Affinity Propagation.