This book has fundamental theoretical and practical aspects of data analysis, useful for beginners and experienced researchers that are looking for a recipe or an analysis approach. Since R has many packages, even experienced researchers look for how particular functions are used in an analysis workflow.
This book has fundamental theoretical and practical aspects of data analysis, useful for beginners and experienced researchers that are looking for a recipe or an analysis approach. Since R has many packages, even experienced researchers look for how particular functions are used in an analysis workflow.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Altuna Akalin is a bioinformatics scientist and the head of Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center in Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. His interest is in using machine learning and statistics to uncover patterns related to important biological variables such as disease state and type. He has lived in the USA, Norway, Turkey, Japan, and Switzerland in order to pursue research work and education related to computational genomics.
Inhaltsangabe
1. Introduction to Genomics. 2. Introduction to R for Genomic Data Analysis 3. Statistics for Genomics 4. Exploratory Data Analysis with Unsupervised Machine Learning 5. Predictive Modeling with Supervised Machine Learning 6. Operations on Genomic Intervals and Genome Arithmetic 7. Quality Check, Processing and Alignment of High-throughput Sequencing Reads 8. RNA-seq Analysis 9. ChIP-seq analysis 10. DNA methylation analysis using bisulfite sequencing 11. Multi-omics Analysis.
1. Introduction to Genomics. 2. Introduction to R for Genomic Data Analysis 3. Statistics for Genomics 4. Exploratory Data Analysis with Unsupervised Machine Learning 5. Predictive Modeling with Supervised Machine Learning 6. Operations on Genomic Intervals and Genome Arithmetic 7. Quality Check, Processing and Alignment of High-throughput Sequencing Reads 8. RNA-seq Analysis 9. ChIP-seq analysis 10. DNA methylation analysis using bisulfite sequencing 11. Multi-omics Analysis.
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