TY  - GEN
AU  - Jiang,Hui
AU  - He,Zhi
AU  - Jiang,Hui
AU  - He,Zhi
TI  - Statistical Methods for the Analysis of Genomic Data
SN  - books978-3-03936-141-0
PY  - 2020///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Research & information: general
KW  - bicssc
KW  - Mathematics & science
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
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/2666
UR  - https://directory.doabooks.org/handle/20.500.12854/68899
ER  -