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020 _a9783031019609
_9978-3-031-01960-9
024 7 _a10.1007/978-3-031-01960-9
_2doi
050 4 _aQH324.2-324.25
072 7 _aPS
_2bicssc
072 7 _aUY
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072 7 _aSCI008000
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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.
300 _aXVI, 140 p. 53 illus., 49 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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