Genome data analysis / Ju Han Kim ; [translated by Younghee Lee].
- 作者： Kim, Ju Han.
- 其他題名：
- Learning materials in biosciences.
- 出版： Singapore : Springer c2019.
- 叢書名： Learning materials in biosciences,
- 主題： Computational Biology--methods , Sequence Analysis, DNA , Databases, Genetic , Oligonucleotide Array Sequence Analysis
- ISBN： 9789811319419 (pbk.): NT2022
- 一般註：Includes bibliographical references.
- 語文註：Translated from the Korean.
-
讀者標籤：
- 系統號： 000290045 | 機讀編目格式
館藏資訊
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.
摘要註
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.