Compressed sensing

Taghouti, Maroua

doi:10.1016/b978-0-12-820488-7.00023-2

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…We proposed that by embracing protease promiscuity could leverage the ubiquity of substrates that recognize multiple targets. Serving as inspiration for the SLICE method, compressed sensing (CS) is a signal processing technique that utilizes measurements of a mixture of multiple target signals to recover information of individual signals 54 . A famous application of CS is the single-pixel camera, which demonstrated the ability to efficiently handle high dimensional datasets (e.g., hyperspectral imaging, video, etc.)…”

Section: Discussionmentioning

confidence: 99%

“…1 , step 5). Rather, SLICE, inspired by the signal processing technique compressed sensing 54–57 , evaluates different combinations of substrates to find the most complementary library that maximally senses all target proteases. We accomplish this by designing a compression score, C , which scores substrate libraries according to two features: (1) substrate orthogonality, which measures the uniqueness of protease-substrate kinetics and (2) protease coverage, which measures the total fraction of target proteases sampled.…”

Section: Introductionmentioning

confidence: 99%

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Embracing enzyme promiscuity with activity-based compressed biosensing

Holt

Lim

et al. 2022

Preprint

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Genome-scale activity-based profiling of proteases requires identifying substrates that are specific to each individual protease. However, this process becomes increasingly difficult as the number of target proteases increases because most substrates are promiscuously cleaved by multiple proteases. We introduce a method - Substrate Libraries for Compressed sensing of Enzymes (SLICE) - for selecting complementary sets of promiscuous substrates to compile libraries that classify complex protease samples (1) without requiring deconvolution of the compressed signals and (2) without the use of highly specific substrates. SLICE ranks substrate libraries according to two features: substrate orthogonality and protease coverage. To quantify these features, we design a compression score that was predictive of classification accuracy across 140 in silico libraries (Pearson r = 0.71) and 55 in vitro libraries (Pearson r = 0.55) of protease substrates. We demonstrate that a library comprising only two protease substrates selected with SLICE can accurately classify twenty complex mixtures of 11 enzymes with perfect accuracy. We envision that SLICE will enable the selection of peptide libraries that capture information from hundreds of enzymes while using fewer substrates for applications such as the design of activity-based sensors for imaging and diagnostics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embracing enzyme promiscuity with activity-based compressed biosensing

Holt

Lim

et al. 2022

Preprint

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“…The Compressed Sensing (CS), also called compressive sensing theory, proposed by Donoho, Tao, and Candes [1], [2] has emerged as an efficient approach to acquiring and reconstructing signals. It exploits the property that all the physical signals are either sparse in the original or on some well-chosen basis (e.g., Fourier or Wavelet transform) to acquire a compressed form of the signal directly, thus performing within a single stage.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing Approach for Physiological Signals: A Review

et al. 2023

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The immense progress in physiological signal acquisition and processing in health monitoring allowed a better understanding of patient disease detection and diagnosis. With the increase in data volume and power consumption, effective data compression, signal acquisition, transmission, and processing techniques are essential, especially in telemonitoring healthcare applications. An emerging research area focuses on integrating Compressed Sensing (CS) with physiological signals to deal with a massive amount of physiological data, transmission bandwidth, and power-saving purposes. A review of compressed sensing for physiological signals is presented in this paper, including electroencephalography (EEG), electrocardiography (EEG), electromyography (EMG), and electrodermal activity (EDA), focusing on the pros and cons of CS in treating such signals and the suitability of CS for hardware implementation. Furthermore, we emphasize performance matrices such as Compression Ratio (CR), Signal to-Noise-Ratio (SNR), Percentage Root-mean-square Difference (PRD), and Processing time to evaluate the performance of CS. We also investigate the current practices, challenges, and opportunities of using CS in healthcare applications.

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