Analysis of class C G-protein coupled receptors using supervised classification methods

G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for pharmacology and has led, over the last decade, to active research in the field of proteomics. The current thesis specifically targets class C of GPCRs, which are relevant in therapies for various central nervous system disorders, such as Alzheimer’s disease, anxiety, Parkinson’s disease and schizophrenia. The investigation of protein functionality often relies on the knowledge of crystal three dimensional (3-D) structures, which determine the receptor’s ability for ligand binding responsible for the activation of certain functionalities in the protein. The structural information is therefore paramount, but it is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as GPCRs. In the face of the lack of information about the 3-D structure, research is often bound to the analysis of the primary amino acid sequences of the proteins, which are commonly known and available from curated databases. Much research on sequence analysis has focused on the quantitative analysis of their aligned versions, although, recently, alternative approaches using machine learning techniques for the analysis of alignment-free sequences have been proposed. In this thesis, we focus on the differentiation of class C GPCRs into functional and structural related subgroups based on the alignment-free analysis of their sequences using supervised classification models. In the first part of the thesis, the main topic is the construction of supervised classification models for unaligned protein sequences based on physicochemical transformations and n-gram representations of their amino acid sequences. These models are useful to assess the internal data quality of the externally labeled dataset and to manage the label noise problem from a data curation perspective. In its second part, the thesis focuses on the analysis of the sequences to discover subtype- and region-speci¿c sequence motifs. For that, we carry out a systematic analysis of the topological sequence segments with supervised classification models and evaluate the subtype discrimination capability of each region. In addition, we apply different types of feature selection techniques to the n-gram representation of the amino acid sequence segments to find subtype and region specific motifs. Finally, we compare the findings of this motif search with the partially known 3D crystallographic structures of class C GPCRs.Los receptores acoplados a proteínas G (GPCRs) son proteínas de la membrana celular con un papel clave para la regulación del funcionamiento de una célula. Esto es consecuencia de su capacidad de transmisión de señales extracelulares, lo que les hace relevante en la farmacología y que ha llevado a investigaciones activas en la última década en el área de la proteómica. Esta tesis se centra específicamente en la clase C de GPCRs, que son relevante para terapias de varios trastornos del sistema nervioso central, como la enfermedad de Alzheimer, ansiedad, enfermedad de Parkinson y esquizofrenia. La investigación de la funcionalidad de proteínas muchas veces se basa en el conocimiento de la estructura cristalina tridimensional (3-D), que determina la capacidad del receptor para la unión con ligandos, que son responsables para la activación de ciertas funcionalidades en la proteína. El análisis de secuencias de amino ácidos se ha centrado en muchas investigaciones en el análisis cuantitativo de las versiones alineados de las secuencias, aunque, recientemente, se han propuesto métodos alternativos usando métodos de aprendizaje automático aplicados a las versiones no-alineadas de las secuencias. En esta tesis, nos centramos en la diferenciación de los GPCRs de la clase C en subgrupos funcionales y estructurales basado en el análisis de las secuencias no-alineadas utilizando modelos de clasificación supervisados. Estos modelos son útiles para evaluar la calidad interna de los datos a partir del conjunto de datos etiquetados externamente y para gestionar el problema del 'ruido de datos' desde la perspectiva de la curación de datos. En su segunda parte, la tesis enfoca el análisis de las secuencias para descubrir motivos de secuencias específicos a nivel de subtipo o región. Para eso, llevamos a cabo un análisis sistemático de los segmentos topológicos de la secuencia con modelos supervisados de clasificación y evaluamos la capacidad de discriminar entre subtipos de cada región. Adicionalmente, aplicamos diferentes tipos de técnicas de selección de atributos a las representaciones mediante n-gramas de los segmentos de secuencias de amino ácidos para encontrar motivos específicos a nivel de subtipo y región. Finalmente, comparamos los descubrimientos de la búsqueda de motivos con las estructuras cristalinas parcialmente conocidas para la clase C de GPCRs

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

Innovations in Medical Image Analysis and Explainable AI for Transparent Clinical Decision Support Systems

This thesis explores innovative methods designed to assist clinicians in their everyday practice, with a particular emphasis on Medical Image Analysis and Explainability issues. The main challenge lies in interpreting the knowledge gained from machine learning algorithms, also called black-boxes, to provide transparent clinical decision support systems for real integration into clinical practice. For this reason, all work aims to exploit Explainable AI techniques to study and interpret the trained models. Given the countless open problems for the development of clinical decision support systems, the project includes the analysis of various data and pathologies. The main works are focused on the most threatening disease afflicting the female population: Breast Cancer. The works aim to diagnose and classify breast cancer through medical images by taking advantage of a first-level examination such as Mammography screening, Ultrasound images, and a more advanced examination such as MRI. Papers on Breast Cancer and Microcalcification Classification demonstrated the potential of shallow learning algorithms in terms of explainability and accuracy when intelligible radiomic features are used. Conversely, the union of deep learning and Explainable AI methods showed impressive results for Breast Cancer Detection. The local explanations provided via saliency maps were critical for model introspection, as well as increasing performance. To increase trust in these systems and aspire to their real use, a multi-level explanation was proposed. Three main stakeholders who need transparent models have been identified: developers, physicians, and patients. For this reason, guided by the enormous impact of COVID-19 in the world population, a fully Explainable machine learning model was proposed for COVID-19 Prognosis prediction exploiting the proposed multi-level explanation. It is assumed that such a system primarily requires two components: 1) inherently explainable inputs such as clinical, laboratory, and radiomic features; 2) Explainable methods capable of explaining globally and locally the trained model. The union of these two requirements allows the developer to detect any model bias, the doctor to verify the model findings with clinical evidence, and justify decisions to patients. These results were also confirmed for the study of coronary artery disease. In particular machine learning algorithms are trained using intelligible clinical and radiomic features extracted from pericoronaric adipose tissue to assess the condition of coronary arteries. Eventually, some important national and international collaborations led to the analysis of data for the development of predictive models for some neurological disorders. In particular, the predictivity of handwriting features for the prediction of depressed patients was explored. Using the training of neural networks constrained by first-order logic, it was possible to provide high-performance and explainable models, going beyond the trade-off between explainability and accuracy

Audio-coupled video content understanding of unconstrained video sequences

Unconstrained video understanding is a difficult task. The main aim of this thesis is to recognise the nature of objects, activities and environment in a given video clip using both audio and video information. Traditionally, audio and video information has not been applied together for solving such complex task, and for the first time we propose, develop, implement and test a new framework of multi-modal (audio and video) data analysis for context understanding and labelling of unconstrained videos. The framework relies on feature selection techniques and introduces a novel algorithm (PCFS) that is faster than the well-established SFFS algorithm. We use the framework for studying the benefits of combining audio and video information in a number of different problems. We begin by developing two independent content recognition modules. The first one is based on image sequence analysis alone, and uses a range of colour, shape, texture and statistical features from image regions with a trained classifier to recognise the identity of objects, activities and environment present. The second module uses audio information only, and recognises activities and environment. Both of these approaches are preceded by detailed pre-processing to ensure that correct video segments containing both audio and video content are present, and that the developed system can be made robust to changes in camera movement, illumination, random object behaviour etc. For both audio and video analysis, we use a hierarchical approach of multi-stage classification such that difficult classification tasks can be decomposed into simpler and smaller tasks. When combining both modalities, we compare fusion techniques at different levels of integration and propose a novel algorithm that combines advantages of both feature and decision-level fusion. The analysis is evaluated on a large amount of test data comprising unconstrained videos collected for this work. We finally, propose a decision correction algorithm which shows that further steps towards combining multi-modal classification information effectively with semantic knowledge generates the best possible results

Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Analysis of class C G-protein coupled receptors using supervised classification methods

G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for pharmacology and has led, over the last decade, to active research in the field of proteomics. The current thesis specifically targets class C of GPCRs, which are relevant in therapies for various central nervous system disorders, such as Alzheimer’s disease, anxiety, Parkinson’s disease and schizophrenia. The investigation of protein functionality often relies on the knowledge of crystal three dimensional (3-D) structures, which determine the receptor’s ability for ligand binding responsible for the activation of certain functionalities in the protein. The structural information is therefore paramount, but it is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as GPCRs. In the face of the lack of information about the 3-D structure, research is often bound to the analysis of the primary amino acid sequences of the proteins, which are commonly known and available from curated databases. Much research on sequence analysis has focused on the quantitative analysis of their aligned versions, although, recently, alternative approaches using machine learning techniques for the analysis of alignment-free sequences have been proposed. In this thesis, we focus on the differentiation of class C GPCRs into functional and structural related subgroups based on the alignment-free analysis of their sequences using supervised classification models. In the first part of the thesis, the main topic is the construction of supervised classification models for unaligned protein sequences based on physicochemical transformations and n-gram representations of their amino acid sequences. These models are useful to assess the internal data quality of the externally labeled dataset and to manage the label noise problem from a data curation perspective. In its second part, the thesis focuses on the analysis of the sequences to discover subtype- and region-speci¿c sequence motifs. For that, we carry out a systematic analysis of the topological sequence segments with supervised classification models and evaluate the subtype discrimination capability of each region. In addition, we apply different types of feature selection techniques to the n-gram representation of the amino acid sequence segments to find subtype and region specific motifs. Finally, we compare the findings of this motif search with the partially known 3D crystallographic structures of class C GPCRs.Los receptores acoplados a proteínas G (GPCRs) son proteínas de la membrana celular con un papel clave para la regulación del funcionamiento de una célula. Esto es consecuencia de su capacidad de transmisión de señales extracelulares, lo que les hace relevante en la farmacología y que ha llevado a investigaciones activas en la última década en el área de la proteómica. Esta tesis se centra específicamente en la clase C de GPCRs, que son relevante para terapias de varios trastornos del sistema nervioso central, como la enfermedad de Alzheimer, ansiedad, enfermedad de Parkinson y esquizofrenia. La investigación de la funcionalidad de proteínas muchas veces se basa en el conocimiento de la estructura cristalina tridimensional (3-D), que determina la capacidad del receptor para la unión con ligandos, que son responsables para la activación de ciertas funcionalidades en la proteína. El análisis de secuencias de amino ácidos se ha centrado en muchas investigaciones en el análisis cuantitativo de las versiones alineados de las secuencias, aunque, recientemente, se han propuesto métodos alternativos usando métodos de aprendizaje automático aplicados a las versiones no-alineadas de las secuencias. En esta tesis, nos centramos en la diferenciación de los GPCRs de la clase C en subgrupos funcionales y estructurales basado en el análisis de las secuencias no-alineadas utilizando modelos de clasificación supervisados. Estos modelos son útiles para evaluar la calidad interna de los datos a partir del conjunto de datos etiquetados externamente y para gestionar el problema del 'ruido de datos' desde la perspectiva de la curación de datos. En su segunda parte, la tesis enfoca el análisis de las secuencias para descubrir motivos de secuencias específicos a nivel de subtipo o región. Para eso, llevamos a cabo un análisis sistemático de los segmentos topológicos de la secuencia con modelos supervisados de clasificación y evaluamos la capacidad de discriminar entre subtipos de cada región. Adicionalmente, aplicamos diferentes tipos de técnicas de selección de atributos a las representaciones mediante n-gramas de los segmentos de secuencias de amino ácidos para encontrar motivos específicos a nivel de subtipo y región. Finalmente, comparamos los descubrimientos de la búsqueda de motivos con las estructuras cristalinas parcialmente conocidas para la clase C de GPCRs.Postprint (published version

Audio-coupled video content understanding of unconstrained video sequences

Unconstrained video understanding is a difficult task. The main aim of this thesis is to recognise the nature of objects, activities and environment in a given video clip using both audio and video information. Traditionally, audio and video information has not been applied together for solving such complex task, and for the first time we propose, develop, implement and test a new framework of multi-modal (audio and video) data analysis for context understanding and labelling of unconstrained videos. The framework relies on feature selection techniques and introduces a novel algorithm (PCFS) that is faster than the well-established SFFS algorithm. We use the framework for studying the benefits of combining audio and video information in a number of different problems. We begin by developing two independent content recognition modules. The first one is based on image sequence analysis alone, and uses a range of colour, shape, texture and statistical features from image regions with a trained classifier to recognise the identity of objects, activities and environment present. The second module uses audio information only, and recognises activities and environment. Both of these approaches are preceded by detailed pre-processing to ensure that correct video segments containing both audio and video content are present, and that the developed system can be made robust to changes in camera movement, illumination, random object behaviour etc. For both audio and video analysis, we use a hierarchical approach of multi-stage classification such that difficult classification tasks can be decomposed into simpler and smaller tasks. When combining both modalities, we compare fusion techniques at different levels of integration and propose a novel algorithm that combines advantages of both feature and decision-level fusion. The analysis is evaluated on a large amount of test data comprising unconstrained videos collected for this work. We finally, propose a decision correction algorithm which shows that further steps towards combining multi-modal classification information effectively with semantic knowledge generates the best possible results.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Automatic BIRAD scoring of breast cancer mammograms

A computer aided diagnosis system (CAD) is developed to fully characterize and classify mass to benign and malignancy and to predict BIRAD (Breast Imaging Reporting and Data system) scores using mammographic image data. The CAD includes a preprocessing step to de-noise mammograms. This is followed by an active counter segmentation to deforms an initial curve, annotated by a radiologist, to separate and define the boundary of a mass from background. A feature extraction scheme wasthen used to fully characterize a mass by extraction of the most relevant features that have a large impact on the outcome of a patient biopsy. For this thirty-five medical and mathematical features based on intensity, shape and texture associated to the mass were extracted. Several feature selection schemes were then applied to select the most dominant features for use in next step, classification. Finally, a hierarchical classification schemes were applied on those subset of features to firstly classify mass to benign (mass with BIRAD score 2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c). Accuracy of segmentation performance were evaluated by calculating the degree of overlapping between the active counter segmentation and the manual segmentation, and the result was 98.5%. Also reproducibility of active counter 3 using different manual initialization of algorithm by three radiologists were assessed and result was 99.5%. Classification performance was evaluated using one hundred sixty masses (80 masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result for classification of data to benign and malignance was found using a combination of sequential forward floating feature (SFFS) selection and a boosted tree hybrid classifier with Ada boost ensemble method, decision tree learner type and 100 learners’ regression tree classifier, achieving 100% sensitivity and specificity in hold out method, 99.4% in cross validation method and 98.62 % average accuracy in cross validation method. For further sub classification of eighty malignance data with BIRAD score of over 4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with BIRAD score 4c), the best result achieved using the boosted tree with ensemble method bag, decision tree learner type with 200 learners Classification, achieving 100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41% average accuracy for ten times run in cross validation method. Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score 3 were gathered. Which means patient is recommended to be tested in another medical imaging technique and also is recommended to do follow-up in six months. The CAD system was trained with mass with BIRAD score 2 and over 4 also 4 it was further tested using 13 masses with a BIRAD score of 3 and the CAD results are shown to agree with the radiologist’s classification after confirming in six months follow up. The present results demonstrate high sensitivity and specificity of the proposed CAD system compared to prior research. The present research is therefore intended to make contributions to the field by proposing a novel CAD system, consists of series of well-selected image processing algorithms, to firstly classify mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study