Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Learning Bayesian networks based on optimization approaches

Learning accurate classifiers from preclassified data is a very active research topic in machine learning and artifcial intelligence. There are numerous classifier paradigms, among which Bayesian Networks are very effective and well known in domains with uncertainty. Bayesian Networks are widely used representation frameworks for reasoning with probabilistic information. These models use graphs to capture dependence and independence relationships between feature variables, allowing a concise representation of the knowledge as well as efficient graph based query processing algorithms. This representation is defined by two components: structure learning and parameter learning. The structure of this model represents a directed acyclic graph. The nodes in the graph correspond to the feature variables in the domain, and the arcs (edges) show the causal relationships between feature variables. A directed edge relates the variables so that the variable corresponding to the terminal node (child) will be conditioned on the variable corresponding to the initial node (parent). The parameter learning represents probabilities and conditional probabilities based on prior information or past experience. The set of probabilities are represented in the conditional probability table. Once the network structure is constructed, the probabilistic inferences are readily calculated, and can be performed to predict the outcome of some variables based on the observations of others. However, the problem of structure learning is a complex problem since the number of candidate structures grows exponentially when the number of feature variables increases. This thesis is devoted to the development of learning structures and parameters in Bayesian Networks. Different models based on optimization techniques are introduced to construct an optimal structure of a Bayesian Network. These models also consider the improvement of the Naive Bayes' structure by developing new algorithms to alleviate the independence assumptions. We present various models to learn parameters of Bayesian Networks; in particular we propose optimization models for the Naive Bayes and the Tree Augmented Naive Bayes by considering different objective functions. To solve corresponding optimization problems in Bayesian Networks, we develop new optimization algorithms. Local optimization methods are introduced based on the combination of the gradient and Newton methods. It is proved that the proposed methods are globally convergent and have superlinear convergence rates. As a global search we use the global optimization method, AGOP, implemented in the open software library GANSO. We apply the proposed local methods in the combination with AGOP. Therefore, the main contributions of this thesis include (a) new algorithms for learning an optimal structure of a Bayesian Network; (b) new models for learning the parameters of Bayesian Networks with the given structures; and finally (c) new optimization algorithms for optimizing the proposed models in (a) and (b). To validate the proposed methods, we conduct experiments across a number of real world problems. Print version is available at: http://library.federation.edu.au/record=b1804607~S4Doctor of Philosoph

Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

Prédiction de la composition corporelle par modélisation locale et les réseaux bayésiens

The assessment of human body composition is important for evaluating health and nutritional status. Among health issues, overweight and obesity are worldwide problems. Increased fat mass, especially in the trunk location, has been associated with an increased risk of metabolic diseases, such as type 2 diabetes and cardiovascular disease. The lean body mass, especially appendicular muscle mass, is also directly related to health and particularly with the mortality rate. Also, aging is associated with substantial changes in body composition. Reduction in body lean or body fat-free mass occurs during aging (Kyle et al., 2001) together with an increase of body fat related to accumulation of adipose tissues, particularly in abdominal region (Kuk et al., 2009); therefore assessing these changes in segmental body composition may be important because the study will lead to a pre-diagnosis for the prevention of morbidity and mortality risk. Accurate measurements of body composition can be obtained from different methods, such as underwater weighing and dual-energy X-ray absorptiometry (DXA). However, their applications are not always convenient, because they require fixed equipment and they are also time consuming and expensive. As a result, they are not convenient for use as a part of routine clinical examinations or population studies. The potential uses of statistical methods for body composition assessment have been highlighted (Snijder et al., 2006), and several attempts to predict body composition, particularly body fat percentage (BF%), have been made (Gallagher et al., 2000a; Jackson et al., 2002; Mioche et al., 2011b).The first aim in this thesis was to develop a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured anthropometric covariables. We proposed a linear solution published in the British Journal of Nutrition. There are two main advantages in our proposed multivariate approach. The first consists in using very simple covariables, such as body weight and height, because these measurements are easy and not expensive. The usefulness of waist circumference is also investigated and combined with age, height and weight as predictor variables. The second advantage is that the multivariate approach enables to take into account the correlation structure between the responses into account, which is useful for a number of inference tasks, e.g., to give simultaneous confidence regions for all the responses together. Then the prediction accuracy of the multivariate approach is justified by comparing with that of the available univariate models that predict body fat percentage (BF%). With a good accuracy, the multivariate outcomes might then be used in studies necessitating the assessment of metabolic risk factors in large populations.The second aim in this thesis was to study age-related changes in segmental body compositions, associated with anthropometric covariables. Two Bayesian modeling methods are proposed for the exploration of age-related changes. The main advantage of these methods is to propose a surrogate for a longitudinal analysis from the cross-sectional datasets. Moreover, the Bayesian modeling enables to provide a prediction distribution, rather than a simple estimate, this is more relevant for exploring the uncertainty or accuracy problems. Also we can incorporate the previous findings in the prior distribution, by combining it with the datasets, we could obtain more suitable conclusions.The previous predictions were based on models supposing any correlation structure within the variables, the third aim in this thesis was to propose a parsimonious sub-model of the multivariable model described by a Gaussian Bayesian network (GBN), more precisely Crossed Gaussian Bayesian Networks (CGBN). Last and final summary in the thesis.La composition corporelle est importante pour évaluer l'état de santé et le statut nutritionnel d'individus. Le surpoids et l'obésité deviennent des problèmes de santé à l'échelle mondiale. L'accroissement de la masse grasse, notamment celle du tronc, a été associée à une augmentation du risque de maladies métaboliques, telles que le diabète de type 2 et les maladies cardiovasculaires. La masse musculaire, en particulier appendiculaire, est également un indice de santé, et est liée au taux de mortalité. En outre, le vieillissement s'accompagne de changements importants dans la composition corporelle. La masse maigre diminue (Kyle et al., 2001) et la masse grasse augmente, liée à une accumulation de tissus adipeux, en particulier dans la région abdominale (Kuk et al., 2009). Il est donc important d'étudier ces changements en fonction de l'âge pour tenter d'établir un pré-diagnostic et aider à la prévention de la morbidité et de mortalité. La composition corporelle se mesure par différentes méthodes, telles que le pesage sous l'eau ou l'absorption bi-photonique à rayons X (DXA). Cependant, ces méthodes de mesure ne sont pas adaptées pour des populations de taille très grande, car elles nécessitent un équipement fixe, demandent des manipulations longues et sont coûteuses. En revanche, le potentiel de méthodes de prédiction statistique a été mis en évidence pour estimer la composition corporelle (Snijder et al., 2006), et plusieurs modèles ont été proposés pour prédire la composition corporelle, notamment le pourcentage de la masse grasse (BF%) (Gallagher et al.,2000a; Jackson et al., 2002; Mioche et al., 2011b). Le premier objectif de cette thèse est de développer un modèle multivarié à partir de covariables anthropométriques pour prédire simultanément les masses grasse et maigre de différents segments du corps. Pour cela, nous avons proposé une régression linéaire multivariable publiée dans le British Journal of Nutrition. Notre proposition multivariée présente deux avantages principaux. Le premier avantage consiste à utiliser les covariables très simples que sont l'âge, le poids et la taille dont la mesure est facile et peu coûteuse. L'utilité d'ajouter comme covariable le tour de taille a été évaluée. Le deuxième avantage est que l'approche multivariée prend en compte la structure de corrélation entre les variables, ce qui est utile pour certaines études d'inférence où on s'intéresse à des fonctions des variables prédites. La qualité de la précision multivariée a été évaluée par comparaison avec celle des modèles univariés déjà publiés. Nous avons montré que la prédiction multivariée est bonne et que notre approche peut donc être utilisée pour des études de risques métaboliques en grandes populations. Le second objectif de cette thèse est d'étudier l'évolution de la composition corporelle au cours du vieillissement, en tenant compte des covariables anthropométriques. Deux modélisations bayésiennes ont été retenues et développées. Un des avantages principaux de nos propositions est, grâce à une modélisation, de réaliser une analyse longitudinale à partir de données transversales. En outre, la modélisation bayésienne permet de fournir une distribution prédictive, et non pas une simple valeur prédite, ce qui permet d'explorer l'incertitude de la prédiction. Également, des résultats antérieurs ou publiés peuvent être incorporés dans la distribution priore, ce qui conduit à des conclusions plus précises. Les prédictions précédentes sont fondées sur des modèles où la structure de corrélation entre les variables est laissée libre, le troisième objectif de notre travail a été d'imposer une structure de corrélation particulière adaptée au problème. L'avantage est l'utilisation d'un sous-modèle parcimonieux du modèle multivarié précédent. Cette structure est décrite au moyen d'un réseau bayésien gaussien (GBN). [...] Suite et fin du résumé dans la thèse

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

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

Integrative Modeling of Transcriptional Regulation in Response to Autoimmune Desease Therapies

Die rheumatoide Arthritis (RA) und die Multiple Sklerose (MS) werden allgemein als Autoimmunkrankheiten eingestuft. Zur Behandlung dieser Krankheiten werden immunmodulatorische Medikamente eingesetzt, etwa TNF-alpha-Blocker (z.B. Etanercept) im Falle der RA und IFN-beta-Präparate (z.B. Betaferon und Avonex) im Falle der MS. Bis heute sind die molekularen Mechanismen dieser Therapien weitestgehend unbekannt. Zudem ist ihre Wirksamkeit und Verträglichkeit bei einigen Patienten unzureichend. In dieser Arbeit wurde die transkriptionelle Antwort im Blut von Patienten auf jede dieser drei Therapien untersucht, um die Wirkungsweise dieser Medikamente besser zu verstehen. Dabei wurden Methoden der Netzwerkinferenz eingesetzt, mit dem Ziel, die genregulatorischen Netzwerke (GRNs) der in ihrer Expression veränderten Gene zu rekonstruieren. Ausgangspunkt dieser Analysen war jeweils ein Genexpressions- Datensatz. Daraus wurden zunächst Gene gefiltert, die nach Therapiebeginn hoch- oder herunterreguliert sind. Anschließend wurden die genregulatorischen Regionen dieser Gene auf Transkriptionsfaktor-Bindestellen (TFBS) analysiert. Um schließlich GRN-Modelle abzuleiten, wurde ein neuer Netzwerkinferenz-Algorithmus (TILAR) verwendet. TILAR unterscheidet zwischen Genen und TF und beschreibt die regulatorischen Effekte zwischen diesen durch ein lineares Gleichungssystem. TILAR erlaubt dabei Vorwissen über Gen-TF- und TF-Gen-Interaktionen einzubeziehen. Im Ergebnis wurden komplexe Netzwerkstrukturen rekonstruiert, welche die regulatorischen Beziehungen zwischen den Genen beschreiben, die im Verlauf der Therapien differentiell exprimiert sind. Für die Etanercept-Therapie wurde ein Teilnetz gefunden, das Gene enthält, die niedrigere Expressionslevel bei RA-Patienten zeigen, die sehr gut auf das Medikament ansprechen. Die Analyse von GRNs kann somit zu einem besseren Verständnis Therapie-assoziierter Prozesse beitragen und transkriptionelle Unterschiede zwischen Patienten aufzeigen

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines