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access icon free Compressive sensing via sparse difference and fractal and entropy recognition for mass spectrometry sensing data

  • Author(s): Ji-xin Liu 1  and  Quan-sen Sun 1
    • View affiliations
    • Affiliations: 1: School of Computer Science and Technology, Nanjing University of Science and Technology, Room 801, Comprehensive Laboratory Building, No. 200, XiaoLingWei Street, Nanjing 210094, People's Republic of China
  • Source: Volume 7, Issue 3, May 2013, p. 201 – 209
    DOI:  10.1049/iet-spr.2011.0219 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 04/06/2011, Accepted 04/02/2013, Revised 16/11/2012, Published

This study presents a novel compressive sensing (CS) framework to solve the high dimensional mass spectrometry (MS) signal processing in Bioinformatics. As a hot research topic, CS has attracted a great deal of attention in many fields. In theory, high sparsity is one precondition for any CS framework. However, in Bioinformatics, one application bottleneck is that only a few MS data can be considered as sparse. So sparse representation (SR) become necessary. However, this will create a new problem that the SR computation cost will be too huge to MS signal because of its high data dimensionality (usually tens of thousands or more). Therefore the authors propose theconcept ofsparse difference (SD) to realise a new CS framework. Firstly, it canacquire the prior MS information through fractal and entropy recognition. Secondly, the original signal can be perfectly recovered by SD based on the previous recognition result. The feasibility and validity of this CS framework isproved by experiments.

Inspec keywords: compressed sensing; mass spectroscopy; bioinformatics; medical signal processing; fractals; sparse matrices

Other keywords: sparse difference; SR computation cost; high dimensional mass spectrometry signal processing; fractal; compressive sensing; entropy recognition; MS signal processing; bioinformatics; mass spectrometry sensing data; sparse representation; CS framework; high data dimensionality

Subjects: Digital signal processing; Algebra; Signal processing and detection; Biology and medical computing; Algebra; Biomedical measurement and imaging; Biomedical engineering

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