Bioinformatics for Omics Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Bioinformatics for Omics Data PDF full book. Access full book title Bioinformatics for Omics Data by Bernd Mayer. Download full books in PDF and EPUB format.
Author: Bernd Mayer
Publisher: Springer Science+Business Media
ISBN: 9781617790270
Category : Bioinformatics
Languages : en
Pages : 584
Get Book Here
Book Description
Presenting an area of research that intersects with and integrates diverse disciplines, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study.
Author: Bernd Mayer
Publisher: Springer Science+Business Media
ISBN: 9781617790270
Category : Bioinformatics
Languages : en
Pages : 584
Get Book Here
Book Description
Presenting an area of research that intersects with and integrates diverse disciplines, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study.
Author: Bernd Mayer
Publisher: Humana Press
ISBN: 9781617790263
Category : Science
Languages : en
Pages : 0
Get Book Here
Book Description
Presenting an area of research that intersects with and integrates diverse disciplines, including molecular biology, applied informatics, and statistics, among others, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study. Divided into three convenient sections, this detailed volume covers central analysis strategies, standardization and data-management guidelines, and fundamental statistics for analyzing Omics profiles, followed by a section on bioinformatics approaches for specific Omics tracks, spanning genome, transcriptome, proteome, and metabolome levels, as well as an assortment of examples of integrated Omics bioinformatics applications, complemented by case studies on biomarker and target identification in the context of human disease. Written in the highly successful Methods in Molecular BiologyTM series format, chapters contain introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and accessible, Bioinformatics for Omics Data: Methods and Protocols serves as an ideal guide to scientists of all backgrounds and aims to convey the appropriate sense of fascination associated with this research field.
Author: Dani S. Zander
Publisher: Springer Science & Business Media
ISBN: 0387724303
Category : Medical
Languages : en
Pages : 691
Get Book Here
Book Description
This major work, complete with 150 illustrations, many of them in color, bridges the gap between clinical pulmonary pathology and basic molecular science. Through a highly visual approach that features an abundance of tables and diagrams, the book offers a practical disease-based overview. The first two sections of the volume provide the reader with general concepts, terminology and procedures in molecular pathology. The remainder of the volume is subdivided into neoplastic and non-neoplastic lung diseases with detailed chapters covering the current molecular pathology of specific diseases. The book will be essential reading for pathologists, pulmonologists, thoracic surgeons and other health care providers interested in lung disease.
Author: George Tseng
Publisher: Cambridge University Press
ISBN: 1107069114
Category : Mathematics
Languages : en
Pages : 497
Get Book Here
Book Description
Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.
Author: Jeffrey J P Tsai
Publisher: World Scientific
ISBN: 9811203598
Category : Science
Languages : en
Pages : 207
Get Book Here
Book Description
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: Rabinarayan Satpathy
Publisher: John Wiley & Sons
ISBN: 111978560X
Category : Computers
Languages : en
Pages : 439
Get Book Here
Book Description
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Author: George Mias
Publisher: Springer
ISBN: 3319723774
Category : Science
Languages : en
Pages : 397
Get Book Here
Book Description
This book offers a comprehensive introduction to using Mathematica and the Wolfram Language for Bioinformatics. The chapters build gradually from basic concepts and the introduction of the Wolfram Language and coding paradigms in Mathematica, to detailed worked examples derived from typical research applications using Wolfram Language code. The coding examples range from basic sequence analysis, accessing genomic databases, differential gene expression, and machine learning implementations to time series analysis of longitudinal omics experiments, multi-omics integration and building dynamic interactive bioinformatics tools using the Wolfram Language. The topics address the daily bioinformatics needs of a broad audience: experimental users looking to understand and visualize their data, beginner bioinformaticians acquiring coding expertise in providing biological research solutions, and practicing expert bioinformaticians working on omics who wish to expand their toolset to include the Wolfram Language.
Author: Mario Cannataro
Publisher: Elsevier
ISBN: 0128229292
Category : Science
Languages : en
Pages : 270
Get Book Here
Book Description
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: Altuna Akalin
Publisher: CRC Press
ISBN: 1498781861
Category : Mathematics
Languages : en
Pages : 463
Get Book Here
Book Description
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Author: Kim-Anh Lê Cao
Publisher: CRC Press
ISBN: 1000472191
Category : Mathematics
Languages : en
Pages : 331
Get Book Here
Book Description
Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R. Features: Provides a broad and accessible overview of methods for multi-omics data integration Covers a wide range of multivariate methods, each designed to answer specific biological questions Includes comprehensive visualisation techniques to aid in data interpretation Includes many worked examples and case studies using real data Includes reproducible R code for each multivariate method, using the mixOmics package The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.
