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| Author | : Angelo Facchiano |
| Publisher | : Frontiers Media SA |
| Total Pages | : 160 |
| Release | : 2024-02-07 |
| Genre | : Science |
| ISBN | : 2832544452 |
| Author | : Angelo Facchiano |
| Publisher | : Frontiers Media SA |
| Total Pages | : 175 |
| Release | : 2020-06-18 |
| Genre | : |
| ISBN | : 2889637522 |
| Author | : Abedalrhman Alkhateeb |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2023-11-14 |
| Genre | : Science |
| ISBN | : 9783031365010 |
The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
| Author | : Jeffrey J P Tsai |
| Publisher | : World Scientific |
| Total Pages | : 207 |
| Release | : 2019-10-14 |
| Genre | : Computers |
| ISBN | : 9811203598 |
With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level data problems in bioinformatics and biomedical research in this post-genomics era, computational biology has been playing an increasingly important role in paving the way as basis for patient-centric healthcare.Two such areas are: (i) implementing AI algorithms supported by biomedical data would deliver significant benefits/improvements towards the goals of precision medicine (ii) blockchain technology will enable medical doctors to securely and privately build personal healthcare records, and identify the right therapeutic treatments and predict the progression of the diseases.A follow-up in the publication of our book Computation Methods with Applications in Bioinformatics Analysis (2017), topics in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug discovery, RNA-seq analysis, tensor decomposition and Boolean network.
| Author | : Mario Cannataro |
| Publisher | : Elsevier |
| Total Pages | : 270 |
| Release | : 2022-05-12 |
| Genre | : Computers |
| ISBN | : 0128229292 |
Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment. Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more. Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences Brings readers up-to-speed on current trends and methods in a dynamic and growing field Provides academic teachers with a complete resource, covering fundamental concepts as well as applications
| Author | : Gary Hardiman |
| Publisher | : MDPI |
| Total Pages | : 202 |
| Release | : 2020-04-15 |
| Genre | : Science |
| ISBN | : 3039287443 |
A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.
| Author | : Debmalya Barh |
| Publisher | : CRC Press |
| Total Pages | : 721 |
| Release | : 2013-03-26 |
| Genre | : Medical |
| ISBN | : 1466562811 |
With the advent of new technologies and acquired knowledge, the number of fields in omics and their applications in diverse areas are rapidly increasing in the postgenomics era. Such emerging fields—including pharmacogenomics, toxicogenomics, regulomics, spliceomics, metagenomics, and environomics—present budding solutions to combat global challenges in biomedicine, agriculture, and the environment. OMICS: Applications in Biomedical, Agricultural, and Environmental Sciences provides valuable insights into the applications of modern omics technologies to real-world problems in the life sciences. Filling a gap in the literature, it offers a broad, multidisciplinary view of current and emerging applications of omics in a single volume. Written by highly experienced active researchers, each chapter describes a particular area of omics and the associated technologies and applications. Topics covered include: Proteomics, epigenomics, and pharmacogenomics Toxicogenomics and the assessment of environmental pollutants Applications of plant metabolomics Nutrigenomics and its therapeutic applications Microalgal omics and omics approaches in biofuel production Next-generation sequencing and omics technology for transgenic plant analysis Omics approaches in crop improvement Engineering dark-operative chlorophyll synthesis Computational regulomics Omics techniques for the analysis of RNA splicing New fields, including metagenomics, glycomics, and miRNA Breast cancer biomarkers for early detection Environomics strategies for environmental sustainability This timely book explores a wide range of omics application areas in the biomedical, agricultural, and environmental sciences. Throughout, it highlights working solutions as well as open problems and future challenges. Demonstrating the diversity of omics, it introduces readers to state-of-the-art developments and trends in omics-driven research.
| Author | : Thorsten Joachims |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 218 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461509076 |
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
| Author | : Indra Mani |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2024-06-10 |
| Genre | : Science |
| ISBN | : 9789819718436 |
This book introduces the rapidly evolving field of multi-omics in understanding the human microbiome. The book focuses on the technology used to generate multi-omics data, including advances in next-generation sequencing and other high-throughput methods. It also covers the application of artificial intelligence and machine learning algorithms to the analysis of multi-omics data, providing readers with an overview of the powerful computational tools that are driving innovation in this field. The chapter also explores the various bioinformatics databases and tools available for the analysis of multi-omics data. The book also delves into the application of multi-omics technology to the study of microbial diversity, including metagenomics, metatranscriptomics, and metaproteomics. The book also explores the use of these techniques to identify and characterize microbial communities in different environments, from the gut and oral microbiome to the skin microbiome and beyond. Towards theend, it focuses on the use of multi-omics in the study of microbial consortia, including mycology and the viral microbiome. The book also explores the potential of multi-omics to identify genes of biotechnological importance, providing readers with an understanding of the role that this technology could play in advancing biotech research. Finally, the book concludes with a discussion of the clinical applications of multi-omics technology, including its potential to identify disease biomarkers and develop personalized medicine approaches. Overall, this book provides readers with a comprehensive overview of this exciting field, highlighting the potential for multi-omics to transform our understanding of the microbial world.
| Author | : Manish Kumar Gupta |
| Publisher | : Elsevier |
| Total Pages | : 434 |
| Release | : 2024-05-10 |
| Genre | : Science |
| ISBN | : 0443160937 |
Integrative Omics: Concepts, Methodology and Applications provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods to solve problems for the rationalization of genotype to phenotype relationships. The reference includes a range of chapters in a systemic ‘step by step’ manner, which begins with the basic concepts from Omic to Multi Integrative Omics approaches, followed by their full range of approaches, applications, emerging trends, and future trends. All key areas of Omics are covered including biological databases, sequence alignment, pharmacogenomics, nutrigenomics and microbial omics, integrated omics for Food Science and Identification of genes associated with disease, clinical data integration and data warehousing, translational omics as well as omics technology policy and society research. Integrative Omics: Concepts, Methodology and Applications highlights the recent concepts, methodologies, advancements in technologies and is also well-suited for researchers from both academic and industry background, undergraduate and graduate students who are mainly working in the area of computational systems biology, integrative omics and translational science. The book bridges the gap between biological sciences, physical sciences, computer science, statistics, data science, information technology and mathematics by presenting content specifically dedicated to mathematical models of biological systems. Provides a holistic, integrated view of a defining and applying network approach, integrative tools, and methods to solve problems for rationalization of genotype to phenotype relationships Offers an interdisciplinary approach to Databases, data analytics techniques, biological tools, network construction, analysis, modeling, prediction and simulation of biological systems leading to ‘translational research’, i.e., drug discovery, drug target prediction, and precision medicine Covers worldwide methods, concepts, databases, and tools used in the construction of integrated pathways
