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Statistical Genomics

Overview of attention for book
Statistical Genomics
Springer US

Table of Contents

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    Book Overview
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    Chapter 1 Multi-omics Data Deconvolution and Integration: New Methods, Insights, and Translational Implications
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    Chapter 2 Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes
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    Chapter 3 Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE
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    Chapter 4 Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper
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    Chapter 5 Statistical Methods for Integrative Clustering of Multi-omics Data
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    Chapter 6 Analysis of Single-Cell RNA-seq Data
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    Chapter 7 A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data
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    Chapter 8 Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data
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    Chapter 9 Statistical Analysis in ChIP-seq-Related Applications
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    Chapter 10 Bioinformatic and Statistical Analysis of Microbiome Data
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    Chapter 11 Statistical and Computational Methods for Microbial Strain Analysis
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    Chapter 12 Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis
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    Chapter 13 Statistical and Computational Methods for Proteogenomic Data Analysis
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    Chapter 14 Pharmacogenomic and Statistical Analysis
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    Chapter 15 Statistical Methods for Disease Risk Prediction with Genotype Data
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    Chapter 16 Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics
Attention for Chapter 12: Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis
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