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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.
Although we can't usually see them, microbes are essential for every part of human life-indeed all life on Earth. The emerging field of metagenomics offers a new way of exploring the microbial world that will transform modern microbiology and lead to practical applications in medicine, agriculture, alternative energy, environmental remediation, and many others areas. Metagenomics allows researchers to look at the genomes of all of the microbes in an environment at once, providing a "meta" view of the whole microbial community and the complex interactions within it. It's a quantum leap beyond traditional research techniques that rely on studying-one at a time-the few microbes that can be grown in the laboratory. At the request of the National Science Foundation, five Institutes of the National Institutes of Health, and the Department of Energy, the National Research Council organized a committee to address the current state of metagenomics and identify obstacles current researchers are facing in order to determine how to best support the field and encourage its success. The New Science of Metagenomics recommends the establishment of a "Global Metagenomics Initiative" comprising a small number of large-scale metagenomics projects as well as many medium- and small-scale projects to advance the technology and develop the standard practices needed to advance the field. The report also addresses database needs, methodological challenges, and the importance of interdisciplinary collaboration in supporting this new field.
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.
This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. It details the techniques used in collection, storage and analysis of data and their usage in different healthcare solutions. It also discusses the techniques of predictive analysis in early diagnosis of critical diseases. The edited book is divided into four parts – part A discusses introduction to artificial intelligence and machine learning in healthcare; part B highlights different analytical techniques used in healthcare; part C provides various security and privacy mechanisms used in healthcare; and finally, part D exemplifies different tools used in visualization and data analytics.
This book presents a collection of high-quality research papers, presented at the Second International Symposium on Bioinformatics and Biomedicine (BioInfoMed’2022). It offers a comprehensive look into some of the fastest growing fields of science, such as biomedicine, bioinformatics, artificial intelligence, and mathematical modeling. The different chapters of the work include both practical solutions and strictly scientific considerations expanding knowledge about the future bioinformatics and biomedical engineering challenges. We believe that the presented works will have a great impact not only on the development and the application of new methods for modeling, decision making and data mining in healthcare and biomedicine, but also it will provide a source of inspiration for researchers who can implement the proposed methods into their practice and scientific studies.
Since the late 1800s, the discovery of new viruses was a gradual process. Viruses were described one by one using a suite of techniques such as (electron) microscopy and viral culture. Investigators were usually interested in a disease state within an organism, and expeditions in viral ecology were rare. The advent of metagenomics using high-throughput sequencing has revolutionized not only the rate of virus discovery, but also the nature of the discoveries. For example, the viral ecology and etiology of many human diseases are being characterized, non-pathogenic viral commensals are ubiquitous, and the description of environmental viromes is making progress. This Frontiers in Virology Research Topic showcases how metagenomic and bioinformatic approaches have been combined to discover, classify and characterize novel viruses.
Volatiles and Metabolites of Microbes compiles the latest research and advancement in the field of volatiles, metabolites synthesized from the microbial strains such as actinomycetes, bacteria, cyanobacteria, and fungal species and their potential applications in the field of healthcare issue and sustainable agriculture. There is an urgent need to explore new and advanced biological methods for health industries and sustainable agriculture and to protect the environment from environmental pollution or contaminates, global warming, and also control the health of human beings from the side effects of various pharmaceuticals products. Focusing all these factors, Volatiles and Metabolites of Microbes explores new aspects of microorganism in terms of volatiles, enzymes, bioactive compounds synthesized from the microbes and their potential applications in the field of sustainable agriculture and health-related issues Provides a broad aspect about volatiles, bioactive compounds, and secondary metabolites of microbes compiled in one cover Gives the latest research and advancement in the field of volatiles, secondary metabolites, and bioactive compounds synthesized from the different microbial strains Responds to new developments in the detection of the complex compound structures of volatiles Offers insight to a very broad audience in Biotechnology, Applied Microbiology, Agronomy, and Pathology
This book constitutes refereed proceedings of the 12th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November – December 2020. Due to the the COVID-19 pandemic the conference was held online. The 68 papers were thoroughly reviewed and selected from 314 submissions. The papers are organized according to the following topical sections: ​data mining and machine learning; deep learning and applications for industry 4.0; recommender systems; computer vision techniques; decision support and control systems; intelligent management information systems; innovations in intelligent systems; intelligent modeling and simulation approaches for games and real world systems; experience enhanced intelligence to IoT; data driven IoT for smart society; applications of collective intelligence; natural language processing; low resource languages processing; computational collective intelligence and natural language processing.