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© 2019

Genome Data Analysis

Textbook

Part of the Learning Materials in Biosciences book series (LMB)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Bioinformatics for Life and Personal Genome Interpretation

  3. Advanced Microarray Data Analysis

    1. Front Matter
      Pages 77-77
    2. Ju Han Kim
      Pages 79-93
    3. Ju Han Kim
      Pages 95-120
    4. Ju Han Kim
      Pages 159-172
  4. Network Biology, Sequence, Pathway and Ontology Informatics

    1. Front Matter
      Pages 173-173
    2. Ju Han Kim
      Pages 189-211
    3. Ju Han Kim
      Pages 213-232
    4. Ju Han Kim
      Pages 233-246
  5. SNPS, GWAS and CNVS, Informatics for Genome Variants

    1. Front Matter
      Pages 247-247
    2. Ju Han Kim
      Pages 261-280

About this book

Introduction

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.

Keywords

Genome data analysis Bioinformatics Practice in data science Statistics using R Clinical informatics

Authors and affiliations

  1. 1.Division of Biomedical InformaticsSeoul National University College of MedicineSeoulKorea (Republic of)

About the authors

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

Bibliographic information

Industry Sectors
Biotechnology
Pharma