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008			220902s2022 sz | s |||| 0|eng d
020			_a9783031019609
024	7		_a10.1007/978-3-031-01960-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aSaeed, Fahad _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aHigh-Performance Algorithms for Mass Spectrometry-Based Omics _cby Fahad Saeed, Muhammad Haseeb
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVI, 140 páginas) _b53 ilustraciones, 49 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
700	1		_aHaseeb, Muhammad _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031019593
776	0	8	_iPrinted edition: _z9783031019616
776	0	8	_iPrinted edition: _z9783031019623
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01960-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c393898 _d393898