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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Bibliographic Details
Other Authors:	Jiang, Hui (Editor), He, Zhi (Editor)
Format:	Book Chapter
Published:	Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:	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
Online Access:	Get Fullteks DOAB: description of the publication
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Statistical Methods for the Analysis of Genomic Data

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