Differential gene expression graphs: A data structure for classification in DNA microarrays

This paper proposes an innovative data structure to be used as a backbone in designing microarray phenotype sample classifiers. The data structure is based on graphs and it is built from a differential analysis of the expression levels of healthy and diseased tissue samples in a microarray dataset. The proposed data structure is built in such a way that, by construction, it shows a number of properties that are perfectly suited to address several problems like feature extraction, clustering, and classificatio

A graph-based representation of Gene Expression profiles in DNA microarrays

This paper proposes a new and very flexible data model, called gene expression graph (GEG), for genes expression analysis and classification. Three features differentiate GEGs from other available microarray data representation structures: (i) the memory occupation of a GEG is independent of the number of samples used to built it; (ii) a GEG more clearly expresses relationships among expressed and non expressed genes in both healthy and diseased tissues experiments; (iii) GEGs allow to easily implement very efficient classifiers. The paper also presents a simple classifier for sample-based classification to show the flexibility and user-friendliness of the proposed data structur

Exploration of Subjective Color Perceptual-Ability by EEG-Induced Type-2 Fuzzy Classifiers

Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This paper deals with color perceptual-ability measurement of subjects using brain response to basic color (red, green and blue) stimuli. It also attempts to determine subjective ability to recognize the base colors in presence of noise tolerance of the base colors, referred to as recognition tolerance. Because of intra- and inter-session variations in subjective brain signal features for a given color stimulus, there exists uncertainty in perceptual-ability. In addition, small variations in the color stimulus result in wide variations in brain signal features, introducing uncertainty in perceptual-ability of the subject. Type-2 fuzzy logic has been employed to handle the uncertainty in color perceptual-ability measurements due to a) variations in brain signal features for a given color, and b) the presence of colored noise on the base colors. Because of limited power of uncertainty management of interval type-2 fuzzy sets and high computational overhead of its general type-2 counterpart, we developed a semi-general type-2 fuzzy classifier to recognize the base color. It is important to note that the proposed technique transforms a vertical slice based general type-2 fuzzy set into an equivalent interval type-2 counterpart to reduce the computational overhead, without losing the contributions of the secondary memberships. The proposed semi-general type-2 fuzzy sets induced classifier yields superior performance in classification accuracy with respect to existing type-1, type-2 and other well-known classifiers. The brain-understanding of a perceived base or noisy base colors is also obtained by exact low resolution electromagnetic topographic analysis (e-LORETA) software. This is used as the reference for our experimental results of the semi-general type-2 classifier in color perceptual-ability detection. Statistical tests undertaken confirm the superiority of the proposed classifier over its competitors. The proposed technique is expected to have interesting applications in identifying people with excellent color perceptual-ability for chemical, pharmaceutical and textile industries

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Exploring gene expression and protein binding data for gene regulation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Gene expression is a tightly controlled process that is regulated by the epigenetic modifications and a series of interactions between the genes and the proteins across the genome. High-throughput technologies such as microarray and chromatin immunoprecipitation technique followed by the next generation sequencing (ChIP-seq) have enabled researchers to investigate the gene expression profile of large of number of genes and the locations of protein bindings and different epigenetic events at the genome-wide scale. To understand the underlying complex mechanisms that regulate gene expression, the computational biology community has proposed many methodologies and tools over the years to integrate the protein binding data; obtained by ChIP-seq and the gene expression data; generated by microarray technology. However, the integrative analysis is still in its infancy. Effective models that capture the complex characteristics of ChIP-seq data and integrate dynamic interactions between gene expression and regulatory factors across different genomic features are still lacking. This thesis aims to provide robust and reliable methodologies to enable investigation of the relationship between different regulatory mechanisms and gene expression that incorporate the advanced and improved results from the ChIP-seq data and the epigenetic phenomena that are closely related to gene regulation. Here, the Markov Random Field model has been adapted to analyse the binding regions of proteins and epigenetic markers using ChIP-Seq technology where the complex characteristics of the data such as spatial dependency, IP efficiency are taken into consideration while modelling the data and demonstrated how this model along with the pre-analysis steps can improve the binding results. Two models have been proposed where these results are then assimilated in the integrative analyses between ChIP-seq and the gene expression data. Several classification techniques are also included in one of the models to find the association between different epigenetic markers, proteins, genomic features and gene expression profile. The models have been applied to public datasets and the results have been validated. With the proposed models, it has been shown how the dynamic interactions between the regulatory proteins and gene expression can be investigated by integrating sets of genes regulated at successive time-points and different biological or experimental conditions as well as protein binding profiles across the genome. If either the gene expression or the protein binding data is missing as it is often the case, studying the relationship between regulatory factors and gene expression with these models will help the biologists estimate gene expression from the available epigenetics data or assume the underlying epigenetics from the available gene expression data. In short, this thesis brings together different biological tools, data processing techniques, advanced machine learning techniques to make a systematic approach to advancing the state of the art in this important epigenetic field.EPSRC and GlaxoSmithKlin