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High-Performance Algorithms for Mass Spectrometry-Based Omics

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Cover of 'High-Performance Algorithms for Mass Spectrometry-Based Omics'

Table of Contents

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    Book Overview
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    Chapter 1 Need for High-Performance Computing for MS-Based Omics Data Analysis
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    Chapter 2 Introduction to Mass Spectrometry Data
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    Chapter 3 Existing HPC Methods and the Communication Lower Bounds for Distributed-Memory Computations for Mass Spectrometry-Based Omics Data
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    Chapter 4 High-Performance Computing Strategy Using Distributed-Memory Supercomputers
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    Chapter 5 Fast Spectral Pre-processing for Big MS Data
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    Chapter 6 A Easy to Use Generalized Template to Support Development of GPU Algorithms
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    Chapter 7 Computational CPU-GPU Template for Pre-processing of Floating-Point MS Data
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    Chapter 8 G-MSR: A GPU-Based Dimensionality Reduction Algorithm
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    Chapter 9 Re-configurable Hardware for Computational Proteomics
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    Chapter 10 Machine-Learning and the Future of HPC for MS-Based Omics
Attention for Chapter 8: G-MSR: A GPU-Based Dimensionality Reduction Algorithm
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Chapter title
G-MSR: A GPU-Based Dimensionality Reduction Algorithm
Chapter number 8
Book title
High-Performance Algorithms for Mass Spectrometry-Based Omics
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-01960-9_8
Book ISBNs
978-3-03-101959-3, 978-3-03-101960-9
Authors

Saeed, Fahad, Haseeb, Muhammad

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