Statistical Methods for the Analysis of Genomic Data
Jiang, Hui
Statistical Methods for the Analysis of Genomic Data - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (136 p.)
Open Access
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.
Creative Commons
English
books978-3-03936-141-0 9783039361403 9783039361410
10.3390/books978-3-03936-141-0 doi
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
Statistical Methods for the Analysis of Genomic Data - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (136 p.)
Open Access
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.
Creative Commons
English
books978-3-03936-141-0 9783039361403 9783039361410
10.3390/books978-3-03936-141-0 doi
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