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Genome Data Analysis (Learning Materials in Biosciences) de Ju Han Kim
Descripción - Reseña del editor 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. Contraportada 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. BiografÃa del autor Professor. Ju Han Kim, Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul , South Korea.
Detalles del Libro
- Name: Genome Data Analysis (Learning Materials in Biosciences)
- Autor: Ju Han Kim
- Categoria: Libros,Libros universitarios y de estudios superiores,Medicina y ciencias de la salud
- Tamaño del archivo: 15 MB
- Tipos de archivo: PDF Document
- Descargada: 637 times
- Idioma: Español
- Archivos de estado: AVAILABLE
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Genome data analysis springerlink basic data preprocessing with normalization and filtering primary pattern analysis and machine learning algorithms using r and python are demonstrated for geneexpression microarrays genotyping microarrays nextgeneration sequencing data epigenomic data and biological network and semantic analyses
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Analysis of genetic and genomic data sets the analysis of large complex data sets here we provide an overview of machine learning applications for the analysis of genome sequencing data sets including the annotation of sequence elements and epigenetic proteomic or metabolomic data we present considerations and recurrent challenges in the application of supervised
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