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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
Main Author: Jiang, Hui
Other Authors: He, Zhi
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
Multiple Cancer Types
Gee
Machine Learning
Omics Data
Kernel Method
Longitudinal Lipidomics Study
False Discovery Rate Control
N/a
Deep Learning
Lipid-environment Interaction
Uncertainty
Bayes Factor
Bayesian Mixed-effect Model
Cpg Sites
Rna-seq
Gene Set Enrichment Analysis
Classification Boundary
Network Substructure
Penalized Variable Selection
Clustering Analysis
Expectation-maximization Algorithm
Dna Methylation
Integrative Analysis
Mathematics And Science / Bicssc
Convolutional Neural Networks
Gene Regulatory Network
Nonparanormal Graphical Model
Prognosis Modeling
Gaussian Finite Mixture Model
Classification
Feed-forward Neural Networks
Boosting
Gene Expression
Ordinal Responses
Research And Information: General / Bicssc
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (136 p.)
ISBN:9783039361410
9783039361403
books978-3-03936-141-0

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