Méthodes bayésiennes pour l'analyse génétique

Ces dernières années, la génomique a connu un intérêt scientifique grandissant, notamment depuis la publication complète des cartes du génome humain au début des années 2000. A présent, les équipes médicales sont confrontées à un nouvel enjeu : l'exploitation des signaux délivrés par les puces ADN. Ces signaux, souvent de grande taille, permettent de connaître à un instant donné quel est le niveau d'expression des gênes dans un tissu considéré, sous des conditions particulières (phénotype, traitement, ...), pour un individu. Le but de cette recherche est d'identifier des séquences temporelles caractéristiques d'une pathologie, afin de détecter, voire de prévenir, une maladie chez un groupe de patients observés. Les solutions développées dans cette thèse consistent en la décomposition de ces signaux en facteurs élémentaires (ou signatures génétiques) selon un modèle bayésien de mélange linéaire, permettant une estimation conjointe de ces facteurs et de leur proportion dans chaque échantillon. L’utilisation de méthodes de Monte Carlo par chaînes de Markov sera tout particulièrement appropriée aux modèles bayésiens hiérarchiques proposés puisqu'elle permettra de surmonter les difficultés liées à leur complexité calculatoire. ABSTRACT : In the past few years, genomics has received growing scientic interest, particularly since the map of the human genome was completed and published in early 2000's. Currently, medical teams are facing a new challenge: processing the signals issued by DNA microarrays. These signals, often of voluminous size, allow one to discover the level of a gene expression in a given tissue at any time, under specic conditions (phenotype, treatment, ...). The aim of this research is to identify characteristic temporal gene expression proles of host response to a pathogen, in order to detect or even prevent a disease in a group of observed patients. The solutions developed in this thesis consist of the decomposition of these signals into elementary factors (genetic signatures) following a Bayesian linear mixing model, allowing for joint estimation of these factors and their relative contributions to each sample. The use of Markov chain Monte Carlo methods is particularly suitable for the proposed hierarchical Bayesian models. Indeed they allow one to overcome the diculties related to their computational complexity

Applying unmixing to gene expression data for tumor phylogeny inference

<p>Abstract</p> <p>Background</p> <p>While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of "sub-types," each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.</p> <p>Results</p> <p>The present work applies "unmixing" methods, which separate complex data sets into combinations of simpler components, to attempt to gain advantages of both tissue-wide and single-cell approaches to cancer phylogenetics. We develop an unmixing method to infer recurring cell states from microarray measurements of tumor populations and use the inferred mixtures of states in individual tumors to identify possible evolutionary relationships among tumor cells. Validation on simulated data shows the method can accurately separate small numbers of cell states and infer phylogenetic relationships among them. Application to a lung cancer dataset shows that the method can identify cell states corresponding to common lung tumor types and suggest possible evolutionary relationships among them that show good correspondence with our current understanding of lung tumor development.</p> <p>Conclusions</p> <p>Unmixing methods provide a way to make use of both intra-tumor heterogeneity and large probe sets for tumor phylogeny inference, establishing a new avenue towards the construction of detailed, accurate portraits of common tumor sub-types and the mechanisms by which they develop. These reconstructions are likely to have future value in discovering and diagnosing novel cancer sub-types and in identifying targets for therapeutic development.</p

Inférence bayésienne dans des problèmes inverses, myopes et aveugles en traitement du signal et des images

Les activités de recherche présentées concernent la résolution de problèmes inverses, myopes et aveugles rencontrés en traitement du signal et des images. Les méthodes de résolution privilégiées reposent sur une démarche d'inférence bayésienne. Celle-ci offre un cadre d'étude générique pour régulariser les problèmes généralement mal posés en exploitant les contraintes inhérentes aux modèles d'observation. L'estimation des paramètres d'intérêt est menée à l'aide d'algorithmes de Monte Carlo qui permettent d'explorer l'espace des solutions admissibles. Un des domaines d'application visé par ces travaux est l'imagerie hyperspectrale et, plus spécifiquement, le démélange spectral. Le second travail présenté concerne la reconstruction d'images parcimonieuses acquises par un microscope MRFM

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Authentication of Amadeo de Souza-Cardoso Paintings and Drawings With Deep Learning

Art forgery has a long-standing history that can be traced back to the Roman period and has become more rampant as the art market continues prospering. Reports disclosed that uncountable artworks circulating on the art market could be fake. Even some principal art museums and galleries could be exhibiting a good percentage of fake artworks. It is therefore substantially important to conserve cultural heritage, safeguard the interest of both the art market and the artists, as well as the integrity of artists’ legacies. As a result, art authentication has been one of the most researched and well-documented fields due to the ever-growing commercial art market in the past decades. Over the past years, the employment of computer science in the art world has flourished as it continues to stimulate interest in both the art world and the artificial intelligence arena. In particular, the implementation of Artificial Intelligence, namely Deep Learning algorithms and Neural Networks, has proved to be of significance for specialised image analysis. This research encompassed multidisciplinary studies on chemistry, physics, art and computer science. More specifically, the work presents a solution to the problem of authentication of heritage artwork by Amadeo de Souza-Cardoso, namely paintings, through the use of artificial intelligence algorithms. First, an authenticity estimation is obtained based on processing of images through a deep learning model that analyses the brushstroke features of a painting. Iterative, multi-scale analysis of the images is used to cover the entire painting and produce an overall indication of authenticity. Second, a mixed input, deep learning model is proposed to analyse pigments in a painting. This solves the image colour segmentation and pigment classification problem using hyperspectral imagery. The result is used to provide an indication of authenticity based on pigment classification and correlation with chemical data obtained via XRF analysis. Further algorithms developed include a deep learning model that tackles the pigment unmixing problem based on hyperspectral data. Another algorithm is a deep learning model that estimates hyperspectral images from sRGB images. Based on the established algorithms and results obtained, two applications were developed. First, an Augmented Reality mobile application specifically for the visualisation of pigments in the artworks by Amadeo. The mobile application targets the general public, i.e., art enthusiasts, museum visitors, art lovers or art experts. And second, a desktop application with multiple purposes, such as the visualisation of pigments and hyperspectral data. This application is designed for art specialists, i.e., conservators and restorers. Due to the special circumstances of the pandemic, trials on the usage of these applications were only performed within the Department of Conservation and Restoration at NOVA University Lisbon, where both applications received positive feedback.A falsificação de arte tem uma história de longa data que remonta ao período romano e tornou-se mais desenfreada à medida que o mercado de arte continua a prosperar. Relatórios revelaram que inúmeras obras de arte que circulam no mercado de arte podem ser falsas. Mesmo alguns dos principais museus e galerias de arte poderiam estar exibindo uma boa porcentagem de obras de arte falsas. Por conseguinte, é extremamente importante conservar o património cultural, salvaguardar os interesses do mercado da arte e dos artis- tas, bem como a integridade dos legados dos artistas. Como resultado, a autenticação de arte tem sido um dos campos mais pesquisados e bem documentados devido ao crescente mercado de arte comercial nas últimas décadas.Nos últimos anos, o emprego da ciência da computação no mundo da arte floresceu à medida que continua a estimular o interesse no mundo da arte e na arena da inteligência artificial. Em particular, a implementação da Inteligência Artificial, nomeadamente algoritmos de aprendizagem profunda (ou Deep Learning) e Redes Neuronais, tem-se revelado importante para a análise especializada de imagens.Esta investigação abrangeu estudos multidisciplinares em química, física, arte e informática. Mais especificamente, o trabalho apresenta uma solução para o problema da autenticação de obras de arte patrimoniais de Amadeo de Souza-Cardoso, nomeadamente pinturas, através da utilização de algoritmos de inteligência artificial. Primeiro, uma esti- mativa de autenticidade é obtida com base no processamento de imagens através de um modelo de aprendizagem profunda que analisa as características de pincelada de uma pintura. A análise iterativa e multiescala das imagens é usada para cobrir toda a pintura e produzir uma indicação geral de autenticidade. Em segundo lugar, um modelo misto de entrada e aprendizagem profunda é proposto para analisar pigmentos em uma pintura. Isso resolve o problema de segmentação de cores de imagem e classificação de pigmentos usando imagens hiperespectrais. O resultado é usado para fornecer uma indicação de autenticidade com base na classificação do pigmento e correlação com dados químicos obtidos através da análise XRF. Outros algoritmos desenvolvidos incluem um modelo de aprendizagem profunda que aborda o problema da desmistura de pigmentos com base em dados hiperespectrais. Outro algoritmo é um modelo de aprendizagem profunda estabelecidos e nos resultados obtidos, foram desenvolvidas duas aplicações. Primeiro, uma aplicação móvel de Realidade Aumentada especificamente para a visualização de pigmentos nas obras de Amadeo. A aplicação móvel destina-se ao público em geral, ou seja, entusiastas da arte, visitantes de museus, amantes da arte ou especialistas em arte. E, em segundo lugar, uma aplicação de ambiente de trabalho com múltiplas finalidades, como a visualização de pigmentos e dados hiperespectrais. Esta aplicação é projetada para especialistas em arte, ou seja, conservadores e restauradores. Devido às circunstâncias especiais da pandemia, os ensaios sobre a utilização destas aplicações só foram realizados no âmbito do Departamento de Conservação e Restauro da Universidade NOVA de Lisboa, onde ambas as candidaturas receberam feedback positivo

Development of a Self-Learning Approach Applied to Pattern Recognition and Fuzzy Control

Systeme auf Basis von Fuzzy-Regeln sind in der Entwicklung der Mustererkennung und Steuersystemen weit verbreitet verwendet. Die meisten aktuellen Methoden des Designs der Fuzzy-Regel-basierte Systeme leiden unter folgenden Problemen 1. Das Verfahren der Fuzzifizierung berücksichtigt weder die statistischen Eigenschaften noch reale Verteilung der betrachteten Daten / Signale nicht. Daher sind die generierten Fuzzy- Zugehörigkeitsfunktionen nicht wirklich in der Lage, diese Daten zu äußern. Darüber hinaus wird der Prozess der Fuzzifizierung manuell definiert. 2. Die ursprüngliche Größe der Regelbasis ist pauschal bestimmt. Diese Feststellung bedeutet, dass dieses Verfahren eine Redundanz in den verwendeten Regeln produzieren kann. Somit wird diese Redundanz zum Auftreten der Probleme von Komplexität und Dimensionalität führen. Der Prozess der Vermeidung dieser Probleme durch das Auswahlverfahren der einschlägigen Regeln kann zum Rechenaufwandsproblem führen. 3. Die Form der Fuzzy-Regel leidet unter dem Problem des Verlusts von Informationen, was wiederum zur Zuschreibung diesen betrachteten Variablen anderen unrealen Bereich führen kann. 4. Ferner wird die Anpassung der Fuzzy- Zugehörigkeitsfunktionen mit den Problemen von Komplexität und Rechenaufwand, wegen der damit verbundenen Iteration und mehrerer Parameter, zugeordnet. Auch wird diese Anpassung im Bereich jeder einzelner Regel realisiert; das heißt, der Anpassungsprozess im Bereich der gesamten Fuzzy-Regelbasis wird nicht durchgeführt

Digital Biomarker Models for Prediction of Infectious Disease Susceptibility

Acute respiratory viral infection (ARVI) represents one of the most prevalent infectious diseases affecting mankind. With the threat of COVID-19 still looming over us, we have witnessed the substantial threat ARVI poses to world health and economy, extinguishing millions of lives and costing trillions of dollars. This sets the context for the research of this thesis: using digital biomarkers to distinguish between individuals who are susceptible to becoming severely infected and/or infectious before an infection is clinically detectable. The development of such biomarkers can have both clinical and epidemiological impact in terms of identifying individuals who are either vulnerable to severe infection or those who may become highly infectious. The digital biomarkers and associated analysis methods are developed and validated on longitudinal data collected by our clinical collaborators from two different ARVI challenge studies. The first study provides data on healthy human volunteers who were inoculated with the common cold and the second study provides data on volunteers inoculated with the flu. Digital biomarkers include molecular, physiological and cognitive data continuously collected from blood, wearable devices and cognitive testing of the study participants. The findings of our research on digitally measurable susceptibility factors are wide-ranging. We find that circadian rhythm at the molecular scale (biochronicity) plays an important role in mediating both the susceptibility and the response to severe infection, revealing groups of gene expression markers that differentiate the responses of low infected and high infected individuals. Using a high dimensional representation of physiological signals from a wearable device, we find that an infection response and its onset time can be reliably predicted at least 24 hours before peak infection time. We find that a certain measure of variability in pre-exposure cognitive function is highly associated with the post-exposure severity of infection.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169966/1/yayazhai_1.pd