A Compressed Sensing Based Basis-pursuit Formulation of the Room Algorithm

Sengupta, Tirthankar; Jain, Shivi; Bhushan, Mani

doi:10.3182/20131216-3-in-2044.00057

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…g ., mean squared error or coefficient of determination). Regularization by means of the ℓ 1 -norm, as generalizations of LASSO, has been already applied in metabolic modeling; for instance, it has been used to reconstruct biochemical networks from time series data [ 27 ], as an alternative to more computationally expensive methods, to study network adaptation to mutations [ 28 ] and, more recently, in FastCORE, one of the existing algorithms to reconstruct context-specific models [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization

Robaina-Estévez

Nikoloski

2015

PLoS ONE

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Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types). Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling.

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Section: Methodsmentioning

confidence: 99%

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization

Robaina-Estévez

Nikoloski

2015

PLoS ONE

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Distributed Signal Processing Algorithms for Wireless Sensor Networks

Chiwarkar¹

2021

IJOSCIENCE

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Wireless Body Area Networks (WBAN), in particular in the field of wearable health monitoring system (WMB), such as electromagnetic cardiograms (ECG) data collecting system via WBANs in e-health applications, is becoming increasingly important for future communication systems. Compressive sensing (CS), on the other hand, has been shown to consume less power compared classic transform-coding-based approaches. We propose a new low-rank sparse deep signal recovery algorithm for recovering ECG data in the context of CS (Compressive sensing) because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain wavelet domains e.g., the discrete wavelet transform (DWT) domain

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Improved method for compressive single-pixel imaging of a moving object

Zha

2021

JCM

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The traditional compressive imaging method for a moving object has the problem of repeated compressive sampling when the object stops moving or remains stationary. To solve this problem, this paper presents an improved method for compressive imaging in this situation. In contrast to traditional methods, the image is preprocessed before reconstruction. First, the Euclidean distance between the current measurement vector and the previous one is calculated, and then a threshold is used to determine whether the new measurement vector is valid. If the distance is below the threshold, the measurement vector is a repetition of the previous one and is discarded; otherwise, the measurement vector is valid and the measurement value is retained and used for image reconstruction. Theoretical analysis and simulation results show that the proposed method can eliminate the repeated sampling problem caused by a pause in the movement of an object, enabling effective reconstruction of the moving object image.

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A Compressed Sensing Based Basis-pursuit Formulation of the Room Algorithm

Cited by 3 publications

References 16 publications

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization

Distributed Signal Processing Algorithms for Wireless Sensor Networks

Improved method for compressive single-pixel imaging of a moving object

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