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Author: Isaac S. Kohane
Publisher: Bradford Books
ISBN: 9780262112710
Category : Science
Languages : en
Pages : 306
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Book Description
An introduction to the use of DNA microarrays in functional genomics.
Author: Isaac S. Kohane
Publisher: Bradford Books
ISBN: 9780262112710
Category : Science
Languages : en
Pages : 306
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Book Description
An introduction to the use of DNA microarrays in functional genomics.
Author: Isaac S. Kohane
Publisher:
ISBN: 9788180520556
Category : Bioinformatics
Languages : en
Pages : 306
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Book Description
Author: Isaac S. Kohane
Publisher:
ISBN:
Category :
Languages : en
Pages : 306
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Book Description
Author: Daniel H. Geschwind
Publisher: MIT Press
ISBN: 9780262072298
Category : Medical
Languages : en
Pages : 366
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Book Description
The effort to sequence the human genome has generated a new discipline, functional genomics, or the study of the relationship between the genetic code and its biologic potential. Gene expression studies are made possible not only by the decoding of the human genome, but by the development of new technologies. The preeminent technology in this area, DNA microarrays, is helping to revolutionize the field of neuroscience.
Author: Ju Han Kim
Publisher: Springer
ISBN: 9811319421
Category : Science
Languages : en
Pages : 367
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Book Description
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Author: Pierre Baldi
Publisher:
ISBN: 9780511071676
Category : DNA microarrays
Languages : en
Pages : 213
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Book Description
DNA microarrays are revolutionizing biology and medicine. They can provide a snapshot of the level of gene expression in a cell and are therefore a powerful tool with which to study biological phenomena at the molecular level. This inter-disciplinary introduction will be essential reading for researchers of all disciplines.
Author: Rabinarayan Satpathy
Publisher: John Wiley & Sons
ISBN: 1119785618
Category : Computers
Languages : en
Pages : 544
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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: Ilya Shmulevich
Publisher: Princeton University Press
ISBN: 1400865263
Category : Science
Languages : en
Pages : 314
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Book Description
Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.
Author: Marilyn S. Arsham
Publisher: John Wiley & Sons
ISBN: 1119061229
Category : Science
Languages : en
Pages : 1216
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Book Description
Cytogenetics is the study of chromosome morphology, structure, pathology, function, and behavior. The field has evolved to embrace molecular cytogenetic changes, now termed cytogenomics. Cytogeneticists utilize an assortment of procedures to investigate the full complement of chromosomes and/or a targeted region within a specific chromosome in metaphase or interphase. Tools include routine analysis of G-banded chromosomes, specialized stains that address specific chromosomal structures, and molecular probes, such as fluorescence in situ hybridization (FISH) and chromosome microarray analysis, which employ a variety of methods to highlight a region as small as a single, specific genetic sequence under investigation. The AGT Cytogenetics Laboratory Manual, Fourth Edition offers a comprehensive description of the diagnostic tests offered by the clinical laboratory and explains the science behind them. One of the most valuable assets is its rich compilation of laboratory-tested protocols currently being used in leading laboratories, along with practical advice for nearly every area of interest to cytogeneticists. In addition to covering essential topics that have been the backbone of cytogenetics for over 60 years, such as the basic components of a cell, use of a microscope, human tissue processing for cytogenetic analysis (prenatal, constitutional, and neoplastic), laboratory safety, and the mechanisms behind chromosome rearrangement and aneuploidy, this edition introduces new and expanded chapters by experts in the field. Some of these new topics include a unique collection of chromosome heteromorphisms; clinical examples of genomic imprinting; an example-driven overview of chromosomal microarray; mathematics specifically geared for the cytogeneticist; usage of ISCN’s cytogenetic language to describe chromosome changes; tips for laboratory management; examples of laboratory information systems; a collection of internet and library resources; and a special chapter on animal chromosomes for the research and zoo cytogeneticist. The range of topics is thus broad yet comprehensive, offering the student a resource that teaches the procedures performed in the cytogenetics laboratory environment, and the laboratory professional with a peer-reviewed reference that explores the basis of each of these procedures. This makes it a useful resource for researchers, clinicians, and lab professionals, as well as students in a university or medical school setting.
Author: David B. Allison
Publisher: CRC Press
ISBN: 1420028790
Category : Mathematics
Languages : en
Pages : 391
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Book Description
Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed by microarrays. In just a few short years the number of statistical papers providing approaches
