National Open University Library

High-performance algorithms for mass spectrometry-based omics / (Record no. 14174)

MARC details
000 -LEADER
fixed length control field 02708cam a2200265 i 4500
INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031019609
INTERNATIONAL STANDARD BOOK NUMBER
ISBN 3031019601
DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number QL368 .S24 2022
Item number 1
MAIN ENTRY--AUTHOR NAME
Personal name Saeed, Fahad,
TITLE STATEMENT
Title High-performance algorithms for mass spectrometry-based omics /
Statement of responsibility, etc Fahad Saeed, Muhammad Haseeb
Copyright Date
Place of publication Cham :
Name of publisher Springer,
Year of publication or production [2022]
Copyright Date
Year of publication or production ©2022
PHYSICAL DESCRIPTION
Number of Pages 1 online resource (xvi, 140 pages) :
Other physical details illustrations (chiefly color)
SERIES STATEMENT
Series statement Computational biology,
FORMATTED CONTENTS NOTE
Formatted contents note 1. Need for High Performance Computing for Big MS Data -- 2. Introduction to Mass Spectrometry Data -- 3. A Review of Spectral Pre-processing -- 4. MS-REDUCE: An Ultra Data Reduction Algorithm -- 5. GPU-DAEMON: A Template to Support Development of GPU Algorithms -- 6. GPU-ArraySort: GPU Based Array Sorting Technique -- 7. G-MSR: A GPU Based Dimensionality Reduction Algorithm -- 8. Simulator Driven Proteomics -- 9. Future and Proposed Work
SUMMARY, ETC.
Summary, etc To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms
SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Mass spectrometry
SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term High performance computing.
SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Computer algorithms.
ADDED ENTRY--PERSONAL NAME
Personal name Haseeb, Muhammad,
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://rave.ohiolink.edu/ebooks/ebc2/9783031019609
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://link.springer.com/10.1007/978-3-031-01960-9
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://go.ohiolink.edu/goto?url=https://link.springer.com/10.1007/978-3-031-01960-9
ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Permanent Location Current Location Date acquired Full call number Accession Number Koha item type
Gabriel Afolabi Ojo Central Library (Headquarters). Gabriel Afolabi Ojo Central Library (Headquarters). 11/07/2024 QL368 .S24 2022 0194729 Books
Gabriel Afolabi Ojo Central Library (Headquarters). Gabriel Afolabi Ojo Central Library (Headquarters). 11/07/2024 QL368 .S24 2022 0194730 Books

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