000 | 03876nam a22005895i 4500 | ||
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001 | 978-3-031-01960-9 | ||
003 | DE-He213 | ||
005 | 20240423130038.0 | ||
007 | cr nn 008mamaa | ||
008 | 220902s2022 sz | s |||| 0|eng d | ||
020 |
_a9783031019609 _9978-3-031-01960-9 |
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024 | 7 |
_a10.1007/978-3-031-01960-9 _2doi |
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050 | 4 | _aQH324.2-324.25 | |
072 | 7 |
_aPS _2bicssc |
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_aUY _2bicssc |
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_aSCI008000 _2bisacsh |
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_aPSAX _2thema |
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082 | 0 | 4 |
_a570.285 _223 |
082 | 0 | 4 |
_a570.113 _223 |
100 | 1 |
_aSaeed, Fahad. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aHigh-Performance Algorithms for Mass Spectrometry-Based Omics _h[electronic resource] / _cby Fahad Saeed, Muhammad Haseeb. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
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300 |
_aXVI, 140 p. 53 illus., 49 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aComputational Biology, _x2662-2432 |
|
505 | 0 | _a1. 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 | _aTo 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 | _aBioinformatics. | |
650 | 0 | _aMass spectroscopy. | |
650 | 0 | _aComputer science. | |
650 | 1 | 4 | _aComputational and Systems Biology. |
650 | 2 | 4 | _aMass Spectrometry. |
650 | 2 | 4 | _aTheory and Algorithms for Application Domains. |
650 | 2 | 4 | _aComputer Science. |
700 | 1 |
_aHaseeb, Muhammad. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031019593 |
776 | 0 | 8 |
_iPrinted edition: _z9783031019616 |
776 | 0 | 8 |
_iPrinted edition: _z9783031019623 |
830 | 0 |
_aComputational Biology, _x2662-2432 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01960-9 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c184439 _d184439 |