國立虎尾科技大學 |

Genome data analysis

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Genome data analysis/ by Ju Han Kim.
Author:	Kim, Ju Han.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xvi, 367 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Genomics. -
Online resource:	https://doi.org/10.1007/978-981-13-1942-6
ISBN:	9789811319426

Genome data analysis
Kim, Ju Han.

Genome data analysis[electronic resource] /by Ju Han Kim. - Singapore :Springer Singapore :2019. - xvi, 367 p. :ill., digital ;24 cm. - Learning materials in biosciences,2509-6125. - Learning materials in biosciences..

Part 1. BIOINFORMATICS FOR LIFE AND PERSONAL GENOME INTERPRETATION -- Chapter 1. Bioinformatics For Life -- Chapter 2. Next Generation Sequencing and Personal Genome Data Analysis -- Chapter 3. Personal Genome Data Analysis -- Chapter 4. Personal Genome Interpretation and Disease Risk Prediction -- Part 2. ADVANCED MICROARRAY DATA ANALYSIS -- Chapter 5. Advanced Microarray Data Analysis -- Chapter 6. Gene Expression Data Analysis -- Chapter 7. Gene Ontology and Biological Pathway-based Analysis -- Chapter 8. Gene-set Approaches and Prognostic Subgroup Prediction -- Chapter 9. MicroRNA Data Analysis -- Part 3. NETWORK BIOLOGY, SEQUENCE, PATHWAY AND ONTOLOGY INFORMATICS -- Chapter 10. Network Biology, Sequence, Pathway and Ontology Informatics -- Chapter 11. Motif and Regulatory Sequence Analysis -- Chapter 12. Molecular Pathways and Gene Ontology -- Chapter 13. Biological Network Analysis -- Part 4. SNPS, GWAS AND CNVS, INFORMATICS FOR GENOME VARIANTS -- Chapter 14. SNPs, GWAS, CNVs: Informatics for Human Genome Variations -- Chapter 15. SNP Data Analysis -- Chapter 16. GWAS Data Analysis -- Chapter 17. CNV Data Analysis -- Part 5. METAGENOME AND EPIGENOME, BASIC DATA ANALYSIS -- Chapter 18. Metagenome and Epigenome Data Analysis -- Chapter 19. Metagenome Data Analysis -- Chapter 20. Epigenome Databases and Tools -- Chapter 21. Epigenome Data Analysis -- Appendix A. BASIC PRACTICE USING R FOR DATA ANALYSIS -- Appendix B. APPLICATION PROGRAM FOR GENOME DATA ANALYSIS INSTALL GUIDE.

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.

ISBN: 9789811319426

Standard No.: 10.1007/978-981-13-1942-6doiSubjects--Topical Terms:

578973
Genomics.

LC Class. No.: QH447 / .K565 2019

Dewey Class. No.: 572.86

Genome data analysis
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505 0 $a Part 1. BIOINFORMATICS FOR LIFE AND PERSONAL GENOME INTERPRETATION -- Chapter 1. Bioinformatics For Life -- Chapter 2. Next Generation Sequencing and Personal Genome Data Analysis -- Chapter 3. Personal Genome Data Analysis -- Chapter 4. Personal Genome Interpretation and Disease Risk Prediction -- Part 2. ADVANCED MICROARRAY DATA ANALYSIS -- Chapter 5. Advanced Microarray Data Analysis -- Chapter 6. Gene Expression Data Analysis -- Chapter 7. Gene Ontology and Biological Pathway-based Analysis -- Chapter 8. Gene-set Approaches and Prognostic Subgroup Prediction -- Chapter 9. MicroRNA Data Analysis -- Part 3. NETWORK BIOLOGY, SEQUENCE, PATHWAY AND ONTOLOGY INFORMATICS -- Chapter 10. Network Biology, Sequence, Pathway and Ontology Informatics -- Chapter 11. Motif and Regulatory Sequence Analysis -- Chapter 12. Molecular Pathways and Gene Ontology -- Chapter 13. Biological Network Analysis -- Part 4. SNPS, GWAS AND CNVS, INFORMATICS FOR GENOME VARIANTS -- Chapter 14. SNPs, GWAS, CNVs: Informatics for Human Genome Variations -- Chapter 15. SNP Data Analysis -- Chapter 16. GWAS Data Analysis -- Chapter 17. CNV Data Analysis -- Part 5. METAGENOME AND EPIGENOME, BASIC DATA ANALYSIS -- Chapter 18. Metagenome and Epigenome Data Analysis -- Chapter 19. Metagenome Data Analysis -- Chapter 20. Epigenome Databases and Tools -- Chapter 21. Epigenome Data Analysis -- Appendix A. BASIC PRACTICE USING R FOR DATA ANALYSIS -- Appendix B. APPLICATION PROGRAM FOR GENOME DATA ANALYSIS INSTALL GUIDE.
520 $a 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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