Statistical Methods for the Analysis of Genomic Data

• Övriga skapare: Jiang, Hui (Utgivare, redaktör, sammanställare), He, Zhi (Utgivare, redaktör, sammanställare)
• Materialtyp: Elektronisk Bokavsnitt
• Språk: engelska
• Publicerad: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Ämnen: Research & information: general; Mathematics & science; multiple cancer types; integrative analysis; omics data; prognosis modeling; classification; gene set enrichment analysis; boosting; kernel method; Bayes factor; Bayesian mixed-effect model; CpG sites; DNA methylation; Ordinal responses; GEE; lipid-environment interaction; longitudinal lipidomics study; penalized variable selection; convolutional neural networks; deep learning; feed-forward neural networks; machine learning; gene regulatory network; nonparanormal graphical model; network substructure; false discovery rate control; gaussian finite mixture model; clustering analysis; uncertainty; expectation-maximization algorithm; classification boundary; gene expression; RNA-seq; n/a
• Länkar: DOAB: download the publication; DOAB: description of the publication

In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational bi...