國立高雄大學圖資館 |

High-performance algorithms for mass spectrometry-based omics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	High-performance algorithms for mass spectrometry-based omicsby Fahad Saeed, Muhammad Haseeb.
作者:	Saeed, Fahad.
其他作者:	Haseeb, Muhammad.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xvi, 140 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Mass spectrometryData processing.
電子資源:	https://doi.org/10.1007/978-3-031-01960-9
ISBN:	9783031019609$q(electronic bk.)

High-performance algorithms for mass spectrometry-based omics
Saeed, Fahad.

High-performance algorithms for mass spectrometry-based omics[electronic resource] /by Fahad Saeed, Muhammad Haseeb. - Cham :Springer International Publishing :2022. - xvi, 140 p. :ill. (chiefly color), digital ;24 cm. - Computational biology,2662-2432. - Computational biology..

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.

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.

ISBN: 9783031019609$q(electronic bk.)

Standard No.: 10.1007/978-3-031-01960-9doiSubjects--Topical Terms:

933404
Mass spectrometry
--Data processing.

LC Class. No.: QD96.M3

Dewey Class. No.: 543.650285

High-performance algorithms for mass spectrometry-based omics
LDR:02839nmm a2200337 a 4500 001 628917
003 DE-He213
005 20220902131916.0
007 cr nn 008maaau
008 230224s2022 sz s 0 eng d
020 $a 9783031019609$q(electronic bk.)
020 $a 9783031019593$q(paper)
024 7 $a 10.1007/978-3-031-01960-9 $2 doi
035 $a 978-3-031-01960-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QD96.M3
072 7 $a PS $2 bicssc
072 7 $a UY $2 bicssc
072 7 $a SCI056000 $2 bisacsh
072 7 $a PSAX $2 thema
082 0 4 $a 543.650285 $2 23
090 $a QD96.M3 $b S127 2022
100 1 $a Saeed, Fahad. $3 933402
245 1 0 $a High-performance algorithms for mass spectrometry-based omics $h [electronic resource] / $c by Fahad Saeed, Muhammad Haseeb.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xvi, 140 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Computational biology, $x 2662-2432
505 0 $a 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.
520 $a 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.
650 0 $a Mass spectrometry $x Data processing. $3 933404
650 0 $a High performance computing. $3 211079
650 0 $a Computer algorithms. $3 184478
650 1 4 $a Computational and Systems Biology. $3 911810
650 2 4 $a Mass Spectrometry. $3 344070
650 2 4 $a Theory and Algorithms for Application Domains. $3 921479
650 2 4 $a Computer Science. $3 212513
700 1 $a Haseeb, Muhammad. $3 933403
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Computational biology. $3 897606
856 4 0 $u https://doi.org/10.1007/978-3-031-01960-9
950 $a Computer Science (SpringerNature-11645)