Genome Data Analysis

2. Auflage 2026
ISBN: 978-981-9238-05-7
Verlag: Springer

Buch, Englisch, Format (B × H): 168 mm x 240 mm

Reihe: Learning Materials in Biosciences

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.

The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

Zielgruppe

Graduate

Autoren/Hrsg.

Kim, Ju Han

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften Systembiologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik

Inhaltsverzeichnis

Part 1. Bioinformatics of Life.- Chapter 1. Bioinformatics of Life.- Chapter 2. Dawn of the bio big data and personal genomics era.- Chapter 3. Interpretation of disease genomics and personal genomic data.- Chapter 4. Pharmacogenomics.- Chapter 5. New technologies for genome analysis.- Chapter 6. Biobank.- Part 2. Information network of Life.- Chapter 7. Motif, ontology, pathway, and network analysis.- Chapter 8. Artificial Intelligence (AI) driven new drug development.- Chapter 9. Analysis of nucleotide sequence motifs and regulation of gene expression.- Chapter 10. Gene ontology and biological pathway analysis.- Chapter 11. Biological network analysis .- Part 3 RNA Functional genomics.- Chapter 12. Understanding microarray data and genotyping analysis.- Chapter 13. Understanding microRNA data analysis.- Chapter 14. Understanding non-coding RNA data analysis.- Chapter 15. Practice of genotyping data analysis.- Chapter 16. Uses of gene ontology and knowledge in biological pathways.- Chapter 17. Gene set analysis and prediction of prognostic subgroups.- Chapter 18. Analysis of microRNA expression.- Part 4. Analysis of DNA genomic variants and genotypes.- Chapter 19. Understanding human genomic polymorphisms.- Chapter 20. Analysis of genomic polymorphisms, Genome-wide association studies (GWAS), and Polygenic risk scores (PRS).- Chapter 21. Rare disease genome data analysis.- Chapter 22. Agriculture, breeding, and data.- Chapter 23. Utilization of SNP and pharmacogenomics database.- Chapter 24. Genome-wide association studies (GWAS) practice.- Chapter 25. Copy-number variation (CNV) data analysis.- Part 5 Analysis of metagenome and epigenome data.- Chapter 26. Epigenome data analysis.- Chapter 27. Microbiome and metagenome.- Chapter 28. Microbiome data analysis.- Chapter 29. Epigenome database and analytical tools.- Chapter 30. Epigenome data analysis.

Über Autor(innen)

Professor Ju Han Kim (MD, PhD) graduated from Seoul National University College of Medicine and trained as a neuropsychiatrist at SNU Hospital and Douglas Potter Fellow at Beth Israel Deaconess Medical Center, Harvard Medical School, followed by an Assistant Professorship in Biomedical Informatics at Harvard Medical School and The Children's Hospital, Boston. He also holds a Master of Engineering in Biomedical Informatics from the Harvard-MIT Division of Health Sciences and Technology, M.I.T. He is currently Professor of Biomedical Informatics and Psychiatry at Seoul National University College of Medicine, and has served as the Executive Vice President for Research at Seoul National University since 2025. He served as President of the Korean Society for Bioinformatics (2013–2014) and Chairman Elected of the Korean Society of Medical Informatics (2027). He has received numerous prestigious awards, including the Yoon Kwang-Ryul Medical Award from the Korean Academy of Medical Sciences (2025) and the WIPO Award for Best Inventor from the World Intellectual Property Organization (2012).