Scalable Big Data Analytics for Protein Bioinformatics

Scalable Big Data Analytics for Protein Bioinformatics
Author :
Publisher : Springer
Total Pages : 315
Release :
ISBN-10 : 9783319988399
ISBN-13 : 3319988395
Rating : 4/5 (99 Downloads)

Book Synopsis Scalable Big Data Analytics for Protein Bioinformatics by : Dariusz Mrozek

Download or read book Scalable Big Data Analytics for Protein Bioinformatics written by Dariusz Mrozek and published by Springer. This book was released on 2018-09-25 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a focus on proteins and their structures. The text describes various scalable solutions for protein structure similarity searching, carried out at main representation levels and for prediction of 3D structures of proteins. Emphasis is placed on techniques that can be used to accelerate similarity searches and protein structure modeling processes. The content of the book is divided into four parts. The first part provides background information on proteins and their representation levels, including a formal model of a 3D protein structure used in computational processes, and a brief overview of the technologies used in the solutions presented in the book. The second part of the book discusses Cloud services that are utilized in the development of scalable and reliable cloud applications for 3D protein structure similarity searching and protein structure prediction. The third part of the book shows the utilization of scalable Big Data computational frameworks, like Hadoop and Spark, in massive 3D protein structure alignments and identification of intrinsically disordered regions in protein structures. The fourth part of the book focuses on finding 3D protein structure similarities, accelerated with the use of GPUs and the use of multithreading and relational databases for efficient approximate searching on protein secondary structures. The book introduces advanced techniques and computational architectures that benefit from recent achievements in the field of computing and parallelism. Recent developments in computer science have allowed algorithms previously considered too time-consuming to now be efficiently used for applications in bioinformatics and the life sciences. Given its depth of coverage, the book will be of interest to researchers and software developers working in the fields of structural bioinformatics and biomedical databases.


Scalable Big Data Analytics for Protein Bioinformatics Related Books

Scalable Big Data Analytics for Protein Bioinformatics
Language: en
Pages: 315
Authors: Dariusz Mrozek
Categories: Computers
Type: BOOK - Published: 2018-09-25 - Publisher: Springer

DOWNLOAD EBOOK

This book presents a focus on proteins and their structures. The text describes various scalable solutions for protein structure similarity searching, carried o
Big Data Analytics in Chemoinformatics and Bioinformatics
Language: en
Pages: 503
Authors: Subhash C. Basak
Categories: Science
Type: BOOK - Published: 2022-12-06 - Publisher: Elsevier

DOWNLOAD EBOOK

Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational
Machine Learning Techniques on Gene Function Prediction Volume II
Language: en
Pages: 264
Authors: Quan Zou
Categories: Science
Type: BOOK - Published: 2023-04-11 - Publisher: Frontiers Media SA

DOWNLOAD EBOOK

Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis
Language: en
Pages: 372
Authors: Stanisław Kozielski
Categories: Computers
Type: BOOK - Published: 2019-05-07 - Publisher: Springer

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 15th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2019, held in
Big Data Analytics in Bioinformatics and Healthcare
Language: en
Pages: 528
Authors: Wang, Baoying
Categories: Computers
Type: BOOK - Published: 2014-10-31 - Publisher: IGI Global

DOWNLOAD EBOOK

As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover pattern