Genome Data Analysis (Learning Materials in Biosciences) - Softcover

Buch 10 von 39: Learning Materials in Biosciences

Kim, Ju Han

 
9789811319419: Genome Data Analysis (Learning Materials in Biosciences)
Softcover
ISBN 10: 9811319413 ISBN 13: 9789811319419
Verlag: Springer, 2019

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Professor. Ju Han Kim, Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul , South Korea.

Von der hinteren Coverseite

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.

This 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.

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Verlag
Springer
Erscheinungsdatum
2019
Sprache
Englisch
ISBN 10 
9811319413
ISBN 13 
9789811319419
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
384
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