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The human microbiota exhibit a highly dynamic composition over the course of life and changes in the human gut microbiota have been associated with human health or disease. Reprogramming of the gut microbiota by interventions that counter these changes and promote long-lasting health has been an emerging topic in microbiome research. Predicting changes in the gut microbiome is therefore crucial for the nature and design of these interventions. Here, we report on a new method based on deep learning to forecast changes in the microbiome. We processed and analyzed nine time-course datasets of the human gut microbiome, identifying the main microorganisms present in these microbial communities at any given time. We then used an encoder-decoder neural network to train a model that successfully predicts the progression of the microbiome composition over time given only five time points of context data. Our results demonstrate the ability to predict the fate of the human gut microbiome into the future, providing the foundation for rational intervention design.
Metagenomic data from human microbiome is a novel source of data for improving diagnosis and prognosis in human diseases. However, to do a prediction based on individual bacteria abundance is a challenge, since the number of features is much bigger than the number of samples. Hence, we face the difficulties related to high dimensional data processing, as well as to the high complexity of heterogeneous data. Machine Learning has obtained great achievements on important metagenomics problems linked to OTU-clustering, binning, taxonomic assignment, etc. The contribution of this PhD thesis is multi-fold: 1) a feature selection framework for efficient heterogeneous biomedical signature extraction, and 2) a novel deep learning approach for predicting diseases using artificial image representations. The first contribution is an efficient feature selection approach based on visualization capabilities of Self-Organizing Maps for heterogeneous data fusion. The framework is efficient on a real and heterogeneous datasets containing metadata, genes of adipose tissue, and gut flora metagenomic data with a reasonable classification accuracy compared to the state-of-the-art methods. The second approach is a method to visualize metagenomic data using a simple fill-up method, and also various state-of-the-art dimensional reduction learning approaches. The new metagenomic data representation can be considered as synthetic images, and used as a novel data set for an efficient deep learning method such as Convolutional Neural Networks. The results show that the proposed methods either achieve the state-of-the-art predictive performance, or outperform it on public rich metagenomic benchmarks.
This book collects and reviews, for the first time, a wide range of advances in the area of human aging biomarkers. This accumulated data allows researchers to assess the rate of aging processes in various organs and systems, and to individually monitor the effectiveness of therapies intended to slow aging. In an introductory chapter, the editor defines biomarkers of aging as molecular, cellular and physiological parameters that demonstrate reproducible changes - quantitative or qualitative - with age. The introduction recounts a study which aimed to create a universal model of biological age, whose most predictive parameters were albumin and alkaline phosphatase (indication liver function), glucose (metabolic syndrome), erythrocytes (respiratory function) and urea (renal function). The book goes on to describe DNA methylation, known as the "epigenetic clock," as currently the most comprehensive predictor of total mortality. It is also useful for predicting mortality from cancer and cardiovascular diseases, and for analyzing the effects of lifestyle factors including diet, exercise, and education. Individual contributions draw additional insight from research on genetics and epigenetic aging markers, and immunosenescence and inflammaging markers. A concluding chapter outlines the challenge of integrating of biological and clinical markers of aging. Biomarkers of Human Aging is written for professionals and practitioners engaged in the study of aging, and will be useful to both advanced students and researchers.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.
In this book, the latest tools available for functional metagenomics research are described.This research enables scientists to directly access the genomes from diverse microbial genomes at one time and study these “metagenomes”. Using the modern tools of genome sequencing and cloning, researchers have now been able to harness this astounding metagenomic diversity to understand and exploit the diverse functions of microorganisms. Leading scientists from around the world demonstrate how these approaches have been applied in many different settings, including aquatic and terrestrial habitats, microbiomes, and many more environments. This is a highly informative and carefully presented book, providing microbiologists with a summary of the latest functional metagenomics literature on all specific habitats.