TY - BOOK ID - 137419527 TI - Statistical Methods for the Analysis of Genomic Data AU - Jiang, Hui AU - He, Zhi PY - 2020 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - multiple cancer types KW - integrative analysis KW - omics data KW - prognosis modeling KW - classification KW - gene set enrichment analysis KW - boosting KW - kernel method KW - Bayes factor KW - Bayesian mixed-effect model KW - CpG sites KW - DNA methylation KW - Ordinal responses KW - GEE KW - lipid–environment interaction KW - longitudinal lipidomics study KW - penalized variable selection KW - convolutional neural networks KW - deep learning KW - feed-forward neural networks KW - machine learning KW - gene regulatory network KW - nonparanormal graphical model KW - network substructure KW - false discovery rate control KW - gaussian finite mixture model KW - clustering analysis KW - uncertainty KW - expectation-maximization algorithm KW - classification boundary KW - gene expression KW - RNA-seq KW - n/a KW - lipid-environment interaction UR - https://www.unicat.be/uniCat?func=search&query=sysid:137419527 AB - 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 biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement. ER -