THE HANDBOOK OF STATISTICAL GENOMICS: Quantitative Methods, Computational Frameworks, and Applications in Modern Genetic Research. - Brossura
Sinossi
Genomic data doesn't interpret itself — and the researchers who master the statistics behind it are the ones driving discovery.
The Handbook of Statistical Genomics is the comprehensive quantitative reference for biostatisticians, computational biologists, geneticists, and data scientists operating in an era defined by whole-genome sequencing, population-scale biobanks, and multi-omics integration. This is the methodological backbone your research has been missing.
What You'll Master:
- Probability frameworks, linkage analysis, and population stratification correction for genetic association studies
- GWAS design, quality control pipelines, and polygenic risk score construction
- Bayesian approaches to variant interpretation and rare variant analysis
- Epistasis modeling, haplotype reconstruction, and admixed population genomics
- Machine learning and deep learning architectures applied to sequence and omics data
- Dimensionality reduction strategies for high-dimensional genomic datasets
- Reproducible bioinformatics pipelines using PLINK, GATK, BOLT-LMM, and R/Bioconductor
- Cutting-edge chapters on single-cell genomics statistics, spatial transcriptomics, and EHR-genomic integration
The most rigorous genomic research starts with the most rigorous statistical foundation.
Secure your copy today and bring the quantitative precision your genomics work deserves.
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THE HANDBOOK OF STATISTICAL GENOMICS
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Paperback. Condizione: new. Paperback. Genomic data doesn't interpret itself - and the researchers who master the statistics behind it are the ones driving discovery.The Handbook of Statistical Genomics is the comprehensive quantitative reference for biostatisticians, computational biologists, geneticists, and data scientists operating in an era defined by whole-genome sequencing, population-scale biobanks, and multi-omics integration. This is the methodological backbone your research has been missing.What You'll Master: Probability frameworks, linkage analysis, and population stratification correction for genetic association studiesGWAS design, quality control pipelines, and polygenic risk score constructionBayesian approaches to variant interpretation and rare variant analysisEpistasis modeling, haplotype reconstruction, and admixed population genomicsMachine learning and deep learning architectures applied to sequence and omics dataDimensionality reduction strategies for high-dimensional genomic datasetsReproducible bioinformatics pipelines using PLINK, GATK, BOLT-LMM, and R/BioconductorCutting-edge chapters on single-cell genomics statistics, spatial transcriptomics, and EHR-genomic integrationEvery method is presented with an emphasis on reproducibility, transparency, and real-world application - so what you learn here translates directly into publication-quality research.The most rigorous genomic research starts with the most rigorous statistical foundation.Secure your copy today and bring the quantitative precision your genomics work deserves. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798199448093
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Paperback. Condizione: new. Paperback. Genomic data doesn't interpret itself - and the researchers who master the statistics behind it are the ones driving discovery.The Handbook of Statistical Genomics is the comprehensive quantitative reference for biostatisticians, computational biologists, geneticists, and data scientists operating in an era defined by whole-genome sequencing, population-scale biobanks, and multi-omics integration. This is the methodological backbone your research has been missing.What You'll Master: Probability frameworks, linkage analysis, and population stratification correction for genetic association studiesGWAS design, quality control pipelines, and polygenic risk score constructionBayesian approaches to variant interpretation and rare variant analysisEpistasis modeling, haplotype reconstruction, and admixed population genomicsMachine learning and deep learning architectures applied to sequence and omics dataDimensionality reduction strategies for high-dimensional genomic datasetsReproducible bioinformatics pipelines using PLINK, GATK, BOLT-LMM, and R/BioconductorCutting-edge chapters on single-cell genomics statistics, spatial transcriptomics, and EHR-genomic integrationEvery method is presented with an emphasis on reproducibility, transparency, and real-world application - so what you learn here translates directly into publication-quality research.The most rigorous genomic research starts with the most rigorous statistical foundation.Secure your copy today and bring the quantitative precision your genomics work deserves. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798199448093
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