Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Improving process monitoring and modeling of batch-type plasma etching tools

Manufacturing equipments in semiconductor factories (fabs) provide abundant data and opportunities for data-driven process monitoring and modeling. In particular, virtual metrology (VM) is an active area of research. Traditional monitoring techniques using univariate statistical process control charts do not provide immediate feedback to quality excursions, hindering the implementation of fab-wide advanced process control initiatives. VM models or inferential sensors aim to bridge this gap by predicting of quality measurements instantaneously using tool fault detection and classification (FDC) sensor measurements. The existing research in the field of inferential sensor and VM has focused on comparing regressions algorithms to demonstrate their feasibility in various applications. However, two important areas, data pretreatment and post-deployment model maintenance, are usually neglected in these discussions. Since it is well known that the industrial data collected is of poor quality, and that the semiconductor processes undergo drifts and periodic disturbances, these two issues are the roadblocks in furthering the adoption of inferential sensors and VM models. In data pretreatment, batch data collected from FDC systems usually contain inconsistent trajectories of various durations. Most analysis techniques requires the data from all batches to be of same duration with similar trajectory patterns. These inconsistencies, if unresolved, will propagate into the developed model and cause challenges in interpreting the modeling results and degrade model performance. To address this issue, a Constrained selective Derivative Dynamic Time Warping (CsDTW) method was developed to perform automatic alignment of trajectories. CsDTW is designed to preserve the key features that characterizes each batch and can be solved efficiently in polynomial time. Variable selection after trajectory alignment is another topic that requires improvement. To this end, the proposed Moving Window Variable Importance in Projection (MW-VIP) method yields a more robust set of variables with demonstrably more long-term correlation with the predicted output. In model maintenance, model adaptation has been the standard solution for dealing with drifting processes. However, most case studies have already preprocessed the model update data offline. This is an implicit assumption that the adaptation data is free of faults and outliers, which is often not true for practical implementations. To this end, a moving window scheme using Total Projection to Latent Structure (T-PLS) decomposition screens incoming updates to separate the harmless process noise from the outliers that negatively affects the model. The integrated approach was demonstrated to be more robust. In addition, model adaptation is very inefficient when there are multiplicities in the process, multiplicities could occur due to process nonlinearity, switches in product grade, or different operating conditions. A growing structure multiple model system using local PLS and PCA models have been proposed to improve model performance around process conditions with multiplicity. The use of local PLS and PCA models allows the method to handle a much larger set of inputs and overcome several challenges in mixture model systems. In addition, fault detection sensitivities are also improved by using the multivariate monitoring statistics of these local PLS/PCA models. These proposed methods are tested on two plasma etch data sets provided by Texas Instruments. In addition, a proof of concept using virtual metrology in a controller performance assessment application was also tested.Chemical Engineerin

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection

BACKGROUND: Acupuncture has been practiced in China for thousands of years as part of the Traditional Chinese Medicine (TCM) and has gradually accepted in western countries as an alternative or complementary treatment. However, the underlying mechanism of acupuncture, especially whether there exists any difference between varies acupoints, remains largely unknown, which hinders its widespread use. RESULTS: In this study, we develop a novel Linear Programming based Feature Selection method (LPFS) to understand the mechanism of acupuncture effect, at molecular level, by revealing the metabolite biomarkers for acupuncture treatment. Specifically, we generate and investigate the high-throughput metabolic profiles of acupuncture treatment at several acupoints in human. To select the subsets of metabolites that best characterize the acupuncture effect for each meridian point, an optimization model is proposed to identify biomarkers from high-dimensional metabolic data from case and control samples. Importantly, we use nearest centroid as the prototype to simultaneously minimize the number of selected features and the leave-one-out cross validation error of classifier. We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR). We find that our LPFS method tends to reveal a small set of metabolites with small standard deviation and large shifts, which exactly serves our requirement for good biomarker. Biologically, several metabolite biomarkers for acupuncture treatment are revealed and serve as the candidates for further mechanism investigation. Also biomakers derived from five meridian points, Zusanli (ST36), Liangmen (ST21), Juliao (ST3), Yanglingquan (GB34), and Weizhong (BL40), are compared for their similarity and difference, which provide evidence for the specificity of acupoints. CONCLUSIONS: Our result demonstrates that metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture. Comparing with other existing methods, LPFS shows better performance to select a small set of key molecules. In addition, LPFS is a general methodology and can be applied to other high-dimensional data analysis, for example cancer genomics

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3

Optimized data processing algorithms for biomarker discovery by LC-MS

This thesis reports techniques and optimization of algorithms to analyse label-free LC-MS data sets for clinical proteomics studies with an emphasis on time alignment algorithms and feature selection methods. The presented work is intended to support ongoing medical and biomarker research. The thesis starts with a review of important steps in a data processing pipeline of label-free Liquid Chromatography – Mass Spectrometry (LC-MS) data. The first part of the thesis discusses an optimization strategy for aligning complex LC-MS chromatograms. It explains the combination of time alignment algorithms (Correlation Optimized Warping, Parametric Time Warping and Dynamic Time Warping) with a Component Detection Algorithm to overcome limitations of the original methods that use Total Ion Chromatograms when applied to highly complex data. A novel reference selection method to facilitate the pre-alignment process and an approach to globally compare the quality of time alignment using overlapping peak area are introduced and used in the study. The second part of this thesis highlights an ongoing challenge faced in the field of biomarker discovery where improvements in instrument resolution coupled with low sample numbers has led to a large discrepancy between the number of measurements and the number of measured variables. A comparative study of various commonly used feature selection methods for tackling this problem is presented. These methods are applied to spiked urine data sets with variable sample size and class separation to mimic typical conditions of biomarker research. Finally, the summary and the remaining challenges in the data processing field are summarized at the end of this thesis.