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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フォーマット:	Online
言語:	英語
出版事項:	MDPI - Multidisciplinary Digital Publishing Institute 2021
主題:	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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science
オンライン･アクセス:	ONIX_20210501_9783039361403_645
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ONIX_20210501_9783039361403_645

Statistical Methods for the Analysis of Genomic Data

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