Statistical Methods for the Analysis of Genomic Data

Statistical Methods for the Analysis of Genomic Data

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...

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Bibliographische Detailangaben
Weitere Verfasser: Jiang, Hui (HerausgeberIn), He, Zhi (HerausgeberIn)
Format: Buchkapitel
Veröffentlicht: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Schlagworte:
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
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