Statistical Methods for the Analysis of Genomic Data

ISBN 10: 3039361406 / ISBN 13: 9783039361403

Published by MDPI AG, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9783039361403

Bibliographic Details

• Title: Statistical Methods for the Analysis of Genomic Data
• Publisher: MDPI AG
• Publication Date: 2020
• Language: English
• ISBN 10: 3039361406
• ISBN 13: 9783039361403
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 14D millimeters 250 millimeters width by 175 millimeters height by 14 millimeters depth
• Weight: 540 grams

About this title

Synopsis

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.