Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

Image processing is a rapidly growing research area of computer science and remains as a challenging problem within the computer vision fields. For the classification of flower images, the problem is mainly due to the huge similarities in terms of colour and texture. The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. The flower image is extracted through Region of Interest (ROI) in which texture and colour are emphasized in this study. In this research, a total of 960 images were extracted from 16 types of flowers. Each ROI was represented by three colour attributes (Hue, Saturation, and Value) and four textures attribute (Contrast, Correlation, Energy and Homogeneity). In training and testing phases, experiments were carried out to observe the classification performance of Neural Networks with duplication of difficult pattern to learn (referred to as DOUBLE) as this could possibly explain as to why some flower images were difficult to learn by classifiers. Results show that the overall performance of Neural Network with DOUBLE is 96.3% while actual data set is 68.3%, and the accuracy obtained from Logistic Regression with actual data set is 60.5%. The Decision Tree classification results indicate that the highest performance obtained by Chi-Squared Automatic Interaction Detection(CHAID) and Exhaustive CHAID (EX-CHAID) is merely 42% with DOUBLE. The findings from this study indicate that Neural Network with DOUBLE data set produces highest performance compared to Logistic Regression and Decision Tree. Therefore, NN has been potential in building Malaysian blooming flower model. Future studies can be focused on increasing the sample size and ROI thus may lead to a higher percentage of accuracy. Nevertheless, the developed flower model can be used as part of the Malaysian Blooming Flower recognition system in the future where the colours and texture are needed in the flower identification process

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Computational systems biology approaches for Parkinson's disease

Parkinson’s disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches

Contextual Analysis of Gene Expression Data

As measurement of gene expression using microarrays has become a standard high throughput method in molecular biology, the analysis of gene expression data is still a very active area of research in bioinformatics and statistics. Despite some issues in quality and reproducibility of microarray and derived data, they are still considered as one of the most promising experimental techniques for the understanding of complex molecular mechanisms. This work approaches the problem of expression data analysis using contextual information. While all analyses must be based on sound statistical data processing, it is also important to include biological knowledge to arrive at biologically interpretable results. After giving an introduction and some biological background, in chapter 2 some standard methods for the analysis of microarray data including normalization, computation of differentially expressed genes, and clustering are reviewed. The first source of context information that is used to aid in the interpretation of the data, is functional annotation of genes. Such information is often represented using ontologies such as gene ontology. GO annotations are provided by many gene and protein databases and have been used to find functional groups that are significantly enriched in differentially expressed, or otherwise conspicuous genes. In gene clustering approaches, functional annotations have been used to find enriched functional classes within each cluster. In chapter 3, a clustering method for the samples of an expression data set is described that uses GO annotations during the clustering process in order to find functional classes that imply a particularly strong separation of the samples. The resulting clusters can be interpreted more easily in terms of GO classes. The clustering method was developed in joint work with Henning Redestig. More complex biological information that covers interactions between biological objects is contained in networks. Such networks can be obtained from public databases of metabolic pathways, signaling cascades, transcription factor binding sites, or high-throughput measurements for the detection of protein-protein interactions such as yeast two hybrid experiments. Furthermore, networks can be inferred using literature mining approaches or network inference from expression data. The information contained in such networks is very heterogenous with respect to the type, the quality and the completeness of the contained data. ToPNet, a software tool for the interactive analysis of networks and gene expression data has been developed in cooperation with Daniel Hanisch. The basic analysis and visualization methods as well as some important concepts of this tool are described in chapter 4. In order to access the heterogeneous data represented as networks with annotated experimental data and functions, it is important to provide advanced querying functionality. Pathway queries allow the formulation of network templates that can include functional annotations as well as expression data. The pathway search algorithm finds all instances of the template in a given network. In order to do so, a special case of the well known subgraph isomorphism problem has to be solved. Although the algorithm has exponential running time in the worst case, some implementation tricks make it run fast enough for practical purposes. Often, a pathway query has many matching instances, and it is important to assess the statistical significance of the individual instances with respect to expression data or other criteria. In chapter 5 the pathway query language and the pathway search algorithm are described in detail and some theoretical properties are derived. Furthermore, some scoring methods that have been implemented are described. The possibility of combining different scoring schemes for different parts of the query result in very flexible scoring capabilities. In chapter 6, some applications of the methods are described, using public data sets as well as data sets from research projects. On the basis of the well studied public data sets, it is demonstrated that the methods yield biologically meaningful results. The other analyses show how new hypotheses can be generated in more complex biological systems, but the validation of these hypotheses can only be provided by new experiments. Finally, an outlook is given on how the presented methods can contribute to ongoing research efforts in the area of expression data analysis, their applicability to other types of data (such as proteomics data) and their possible extensions.Während die Messung von RNA-Konzentrationen mittels Microarrays eine Standardtechnik zur genomweiten Bestimmung von Genexpressionswerten geworden ist, ist die Analyse der dabei gewonnenen Daten immer noch ein Gebiet äußerst aktiver Forschung. Trotz einiger Probleme bezüglich der Reproduzierbarkeit von Microarray- und davon abgeleiteten Daten werden diese als eine der vielversprechendsten Technologien zur Aufklärung komplexer molekularer Mechanismen angesehen. Diese Arbeit beschäftigt sich mit dem Problem der Expressionsdatenanalyse mit Hilfe von Kontextinformationen. Alle Analysen müssen auf solider Statistik beruhen, aber es ist außerdem wichtig, biologisches Wissen einzubeziehen, um biologisch interpretierbare Ergebnisse zu erhalten. Nach einer Einleitung und einigem biologischen Hintergrund werden in Kapitel 2 einige Standardmethoden zur Analyse von Expressionsdaten vorgestellt, wie z.B. Normalisierung, Berechnung differenziell exprimierter Gene sowie Clustering. Die erste Quelle von Kontextinformationen, die zur besseren Interpretation der Daten herangezogen wird, ist funktionale Annotation von Genen. Solche Informationen werden oft mit Hilfe von Ontologien wie z.B. der Gene Ontology dargestellt. GO Annotationen werden von vielen Gen- und Proteindatenbanken zur Verfügung gestellt und werden unter anderem benutzt, um Funktionen zu finden, die signifikant angereichert sind an differenziell exprimierten oder aus anderen Gründen auffälligen Genen. Bei Clusteringmethoden werden funktionale Annotationen benutzt, um in den gefundenen Clustern angereicherte Funktionen zu identifizieren. In Kapitel 3 wird ein neues Clusterverfahren für Proben in Expressionsdatensätzen vorgestellt, das GO Annotationen während des Clustering benutzt, um Funktionen zu finden, anhand derer die Expressionsdaten besonders deutlich getrennt werden können. Die resultierenden Cluster können mit Hilfe der GO Annotationen leichter interpretiert werden. Die Clusteringmethode wurde in Zusammenarbeit mit Henning Redestig entwickelt. Komplexere biologische Informationen, die auch die Interaktionen zwischen biologischen Objekten beinhaltet, sind in Netzwerken enthalten. Solche Netzwerke können aus öffentlichen Datenbanken von metabolischen Pfaden, Signalkaskaden, Bindestellen von Transkriptionsfaktoren, aber auch aus Hochdurchsatzexperimenten wie der Yeast Two Hybrid Methode gewonnen werden. Außerdem können Netzwerke durch die automatische Auswertung wissenschaftlicher Literatur oder Inferenz aus Expressionsdaten gewonnen werden. Die Information, die in solchen Netzwerken enthalten ist, ist sehr verschieden in Bezug auf die Art, die Qualität und die Vollständigkeit der Daten. ToPNet, ein Computerprogramm zur interaktiven Analyse von Netzwerken und Genexpressionsdaten, wurde gemeinsam mit Daniel Hanisch entwickelt. Die grundlegenden Analyse und Visualisierungsmethoden sowie einige wichtige Konzepte dieses Programms werden in Kapitel 4 beschrieben. Um auf die verschiedenartigen Daten zugreifen zu können, die durch Netzwerke mit funktionalen Annotationen sowie Expressionsdaten repräsentiert werden, ist es wichtig, flexible und mächtige Anfragefunktionalität zur Verfügung zu stellen. Pathway queries erlauben die Beschreibung von Netzwerkmustern, die funktionale Annotationen sowie Expressionsdaten enthalten. Der pathway search Algorithmus findet alle Instanzen des Musters in einem gegebenen Netzwerk. Dazu muss ein Spezialfall des bekannten Subgraph-Isomorphie-Problems gelöst werden. Obwohl der Algorithmus im schlechtesten Fall exponentielle Laufzeit in der Größe des Musters hat, läuft er durch einige Implementationstricks schnell genug für praktische Anwendungen. Oft hat eine pathway query viele Instanzen, so dass es wichtig ist, die statistische Signifikanz der einzelnen Instanzen in Hinblick auf Expressionsdaten oder andere Kriterien zu bestimmen. In Kapitel 5 werden die Anfragesprache pathway query language sowie der pathway search Algorithmus im Detail vorgestellt und einige theoretische Eigenschaften gezeigt. Außerdem werden einige implementierte Scoring-Methoden beschrieben. Die Möglichkeit, verschiedene Teile der Anfrage mit verschiedenen Scoring-Methoden zu bewerten und zu einem Gesamtscore zusammenzufassen, erlaubt äußerst flexible Bewertungen der Instanzen. In Kapitel 6 werden einige Anwendungen der vorgestellten Methoden beschrieben, die auf öffentlichen Datensätzen sowie Datensätzen aus Forschungsprojekten beruhen. Mit Hilfe der gut untersuchten öffentlichen Datensätze wird gezeigt, dass die Methoden biologisch sinnvolle Ergebnisse liefern. Die anderen Analysen zeigen, wie neue Hypothesen in komplexeren biologischen Systemen generiert werden können, die jedoch nur mit Hilfe von weiteren biologischen Experimenten validiert werden könnten. Schließlich wird ein Ausblick gegeben, was die vorgestellten Methoden zur laufenden Forschung im Bereich der Expressionsdatenanalyse beitragen können, wie sie auf andere Daten angewendet werden können und welche Erweiterungen denkbar und wünschenswert sind

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise

Automatic production and integration of knowledge to the support of the decision and planning activities in medical-clinical diagnosis, treatment and prognosis.

El concepto de procedimiento médico se refiere al conjunto de actividades seguidas por los profesionales de la salud para solucionar o mitigar el problema de salud que afecta a un paciente. La toma de decisiones dentro del procedimiento médico ha sido, por largo tiempo, uno de las áreas más interesantes de investigación en la informática médica y el contexto de investigación de esta tesis. La motivación para desarrollar este trabajo de investigación se basa en tres aspectos fundamentales: no hay modelos de conocimiento para todas las actividades médico-clínicas que puedan ser inducidas a partir de datos médicos, no hay soluciones de aprendizaje inductivo para todas las actividades de la asistencia médica y no hay un modelo integral que formalice el concepto de procedimiento médico. Por tanto, nuestro objetivo principal es desarrollar un modelo computable basado en conocimiento que integre todas las actividades de decisión y planificación para el diagnóstico, tratamiento y pronóstico médico-clínicos. Para alcanzar el objetivo principal, en primer lugar, explicamos el problema de investigación. En segundo lugar, describimos los antecedentes del problema de investigación desde los contextos médico e informático. En tercer lugar, explicamos el desarrollo de la propuesta de investigación, basada en cuatro contribuciones principales: un nuevo modelo, basado en datos y conocimiento, para la actividad de planificación en el diagnóstico y tratamiento médico-clínicos; una novedosa metodología de aprendizaje inductivo para la actividad de planificación en el diagnóstico y tratamiento médico-clínico; una novedosa metodología de aprendizaje inductivo para la actividad de decisión en el pronóstico médico-clínico, y finalmente, un nuevo modelo computable, basado en datos y conocimiento, que integra las actividades de decisión y planificación para el diagnóstico, tratamiento y pronóstico médico-clínicos.The concept of medical procedure refers to the set of activities carried out by the health care professionals to solve or mitigate the health problems that affect a patient. Decisions making within a medical procedure has been, for a long time, one of the most interesting research areas in medical informatics and the research context of this thesis. The motivation to develop this research work is based on three main aspects: Nowadays there are not knowledge models for all the medical-clinical activities that can be induced from medical data, there are not inductive learning solutions for all the medical-clinical activities, and there is not an integral model that formalizes the concept of medical procedure. Therefore, our main objective is to develop a computable model based in knowledge that integrates all the decision and planning activities for the medical-clinical diagnosis, treatment and prognosis. To achieve this main objective: first, we explain the research problem. Second, we describe the background of the work from both the medical and the informatics contexts. Third, we explain the development of the research proposal based on four main contributions: a novel knowledge representation model, based in data, to the planning activity in medical-clinical diagnosis and treatment; a novel inductive learning methodology to the planning activity in diagnosis and medical-clinical treatment; a novel inductive learning methodology to the decision activity in medical-clinical prognosis, and finally, a novel computable model, based on data and knowledge, which integrates the decision and planning activities of medical-clinical diagnosis, treatment and prognosis