The large potential of RNA sequencing and other "omics" techniques has contributed to the production of a huge amount of data pursuing to answer many different questions that surround the science's great unknowns. This book presents an overview about powerful and cost-efficient methods for a comprehensive analysis of RNA-Seq data, introducing and revising advanced concepts in data analysis using the most current algorithms. A holistic view about the entire context where transcriptome is inserted is also discussed here encompassing biological areas with remarkable technological advances in the study of systems biology, from microorganisms to precision medicine.

The book contains contributions concerning the application of the new instrumental and methodological developments in omics technologies, including those related to Genomics, Transcriptomics, Proteomics, Peptidomics and Metabolomics, Lipidomics and Foodomics. The16 chapters discuss in detail: innovative applications of functional gene microarrays for profiling microbial communities, microRNA profiling, novel genotyping applications using microarray technology in cancer research, next-generation sequencing applied to the study of human microbiome, emerging RNA-SEQ applications in food science, recent progress in plant proteomics, applications of gel-free proteomic approaches, the challenges and applications of proteomics tools for food authenticity, the role of salivary peptidomics in clinical applications, metabolomic approaches to the study of degenerative, cardiovascular and renal diseases, and neonatal medicine. Also covered are other omics applications such as profiling of genetically modified organisms, the fundamentals, applications and challenges of foodomics, and MS-based lipidomics. Moreover, this volume includes relevant and updated aspects on bioinformatics, data treatment, data integration and systems biology. This book complements the previous volume "Fundamentals of Advanced Omics Technologies: New Advances from Genes to Metabolites" that covered the fundamental aspects of these new omics technologies. - Describes the latest applications of omics technologies - Provides an excellent reference for applications of advanced omics techniques - Includes advanced tools and methodologies for dealing with the data generated

Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. Describes a range of state of the art omics analytical platforms Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

This book provides a unique perspective on the biomedical and societal implications of personalized medicine and how it helps to mitigate the healthcare crisis and rein in ever-growing expenditure. It introduces the reader to the underlying concepts at the heart of personalized medicine. An innovative second edition, this book functions as an update to the successful first edition to include new, state-of-the-art information and advancements in the fast-paced field of personalized medicine. Chapters examine pharmacogenomics, targeted therapies, individualized diagnosis and treatment, and cancer immunotherapies. The book also features an essential discussion on how the advent of genomic technologies gives clinicians the capability to predict and diagnose disease more efficiently and offers a detailed up-to-date compilation of clinical trials in cancer leading to breakthrough therapies. The book also addresses the impact of Big Data on personalized medicine and the newfound applications of digital health and artificial intelligence. A work that advocates for a patient-centered approach, Advancing Healthcare Through Personalized Medicine, Second Edition is an invaluable text for clinicians, healthcare providers, and patients.

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.

The edited book highlights various emerging Omics tools and techniques that are currently being used in the analysis of responses to different abiotic stress in agronomically important cereals and their applications in enhancing tolerance mechanism. Plants are severely challenged by diverse abiotic stress factors such as low water availability (drought), excess water (flooding/ waterlogging), extremes of temperatures (cold, chilling, frost, and heat), salinity, mineral deficiency, and heavy metal toxicity. Agronomically important cereal crops like Rice, Wheat, Maize, Sorghum, Pearl Millet, Barley, Oats, Rye, Foxtail Millets etc. that are the major sources of food material and nutritional components for human health are mostly exposed to abiotic stresses during the critical phases of flowering and grain yield. Different Omics platforms like genomics, transcriptomics proteomics, metabolomics and phenomics, in conjunction with breeding and transgenic technology, and high throughput technologies like next generation sequencing, epigenomics, genome editing and CRISPR-Cas technology have emerged altogether in understanding abiotic stress response and strengthening defense and tolerance mechanism of different cereals. This book is beneficial to different universities and research institutes working with different cereal crops in the areas of stress physiology, stress-associated genes and proteins, genomics, proteomics, genetic engineering, and other fields of molecular plant physiology. The book can also be used as advanced textbook for the course work of research and master’s level students. It will be of use to people involved in ecological studies and sustainable agriculture. The proposed book bring together the global leaders working on environmental stress in different cereal crops and motivate scientists to explore new horizons in the relevant areas of research.

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.