語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
High-performance algorithms for mass...
~
Saeed, Fahad.
FindBook
Google Book
Amazon
博客來
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 omics/ by 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.
內容註:
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.
Contained By:
Springer Nature eBook
標題:
Mass spectrometry - Data processing. -
電子資源:
https://doi.org/10.1007/978-3-031-01960-9
ISBN:
9783031019609
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
Standard No.: 10.1007/978-3-031-01960-9doiSubjects--Topical Terms:
3604009
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
2303092
003
DE-He213
005
20220902131916.0
007
cr nn 008maaau
008
230409s2022 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
3604007
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
3604009
650
0
$a
High performance computing.
$3
591827
650
0
$a
Computer algorithms.
$3
523872
650
1 4
$a
Computational and Systems Biology.
$3
3531279
650
2 4
$a
Mass Spectrometry.
$3
683779
650
2 4
$a
Theory and Algorithms for Application Domains.
$3
3594704
650
2 4
$a
Computer Science.
$3
626642
700
1
$a
Haseeb, Muhammad.
$3
3604008
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
Computational biology.
$3
3505128
856
4 0
$u
https://doi.org/10.1007/978-3-031-01960-9
950
$a
Computer Science (SpringerNature-11645)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9444641
電子資源
11.線上閱覽_V
電子書
EB QD96.M3
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入